chore(hq): daily sync 2026-02-23

Closes the optimization loop: OP2 results → density field refinement.

**extract_stress_field_2d.py (new)**
- Reads OP2 (3D solid or 2D shell elements) + BDF via pyNastran
- Projects element centroids to 2D sandbox coords using geometry transform
- Averages stress through thickness (for solid 3D meshes)
- Normalises by sigma_yield to [0..1]
- save/load helpers (NPZ) for trial persistence

**stress_feedback.py (new)**
- StressFeedbackField: converts 2D stress scatter → smooth density modifier
- Gaussian blur (configurable radius, default 40mm) prevents oscillations
- RBF interpolator (thin-plate spline) for fast pointwise evaluation
- evaluate(x, y) returns S_stress ∈ [0..1]
- from_field() and from_npz() constructors

**density_field.py (modified)**
- evaluate_density() now accepts optional stress_field= argument
- Adaptive formula: η = η₀ + α·I + β·E + γ·S_stress
- gamma_stress param controls feedback gain (0.0 = pure parametric)
- Fully backward compatible (no stress_field = original behaviour)

Usage:
    field = extract_stress_field_2d(op2, bdf, geometry["transform"], sigma_yield=276.0)
    feedback = StressFeedbackField.from_field(field, blur_radius_mm=40.0)
    eta = evaluate_density(x, y, geometry, params, stress_field=feedback)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.claude-mcp-06fea803.json

@@ -1 +0,0 @@
-{"mcpServers": {"atomizer": {"command": "node", "args": ["C:\\Users\\antoi\\Atomizer\\mcp-server\\atomizer-tools\\dist\\index.js"], "env": {"ATOMIZER_MODE": "user", "ATOMIZER_ROOT": "C:\\Users\\antoi\\Atomizer"}}}}

.claude-mcp-3e104790.json

@@ -1 +0,0 @@
-{"mcpServers": {"atomizer": {"command": "node", "args": ["C:\\Users\\antoi\\Atomizer\\mcp-server\\atomizer-tools\\dist\\index.js"], "env": {"ATOMIZER_MODE": "user", "ATOMIZER_ROOT": "C:\\Users\\antoi\\Atomizer"}}}}

.claude-prompt-06fea803.md

@@ -1,45 +0,0 @@
-# Atomizer Assistant
-
-You are the Atomizer Assistant - an expert system for structural optimization using FEA.
-
-**Current Mode**: USER
-
-Your role:
-- Help engineers with FEA optimization workflows
-- Create, configure, and run optimization studies
-- Analyze results and provide insights
-- Explain FEA concepts and methodology
-
-Important guidelines:
-- Be concise and professional
-- Use technical language appropriate for engineers
-- You are "Atomizer Assistant", not a generic AI
-- Use the available MCP tools to perform actions
-- When asked about studies, use the appropriate tools to get real data
-
-
----
-
-# Current Study: m1_mirror_flatback_lateral
-
-**Status**: Study directory not found.
-
----
-
-# User Mode Instructions
-
-You can help with optimization workflows:
-- Create and configure studies
-- Run optimizations
-- Analyze results
-- Generate reports
-- Explain FEA concepts
-
-**For code modifications**, suggest switching to Power Mode.
-
-Available tools:
-- `list_studies`, `get_study_status`, `create_study`
-- `run_optimization`, `stop_optimization`, `get_optimization_status`
-- `get_trial_data`, `analyze_convergence`, `compare_trials`, `get_best_design`
-- `generate_report`, `export_data`
-- `explain_physics`, `recommend_method`, `query_extractors`

.claude-prompt-3e104790.md

@@ -1,45 +0,0 @@
-# Atomizer Assistant
-
-You are the Atomizer Assistant - an expert system for structural optimization using FEA.
-
-**Current Mode**: USER
-
-Your role:
-- Help engineers with FEA optimization workflows
-- Create, configure, and run optimization studies
-- Analyze results and provide insights
-- Explain FEA concepts and methodology
-
-Important guidelines:
-- Be concise and professional
-- Use technical language appropriate for engineers
-- You are "Atomizer Assistant", not a generic AI
-- Use the available MCP tools to perform actions
-- When asked about studies, use the appropriate tools to get real data
-
-
----
-
-# Current Study: m1_mirror_flatback_lateral
-
-**Status**: Study directory not found.
-
----
-
-# User Mode Instructions
-
-You can help with optimization workflows:
-- Create and configure studies
-- Run optimizations
-- Analyze results
-- Generate reports
-- Explain FEA concepts
-
-**For code modifications**, suggest switching to Power Mode.
-
-Available tools:
-- `list_studies`, `get_study_status`, `create_study`
-- `run_optimization`, `stop_optimization`, `get_optimization_status`
-- `get_trial_data`, `analyze_convergence`, `compare_trials`, `get_best_design`
-- `generate_report`, `export_data`
-- `explain_physics`, `recommend_method`, `query_extractors`

.claude/settings.local.json

@@ -62,7 +62,26 @@
       "Bash(xargs -I{} git ls-tree -r -l HEAD {})",
       "Bash(sort:*)",
       "Bash(C:Usersantoianaconda3envsatomizerpython.exe introspect_model.py)",
-      "Bash(xargs:*)"
+      "Bash(xargs:*)",
+      "Bash(ping:*)",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe -c \"import requests; r = requests.post\\(''http://127.0.0.1:8001/api/claude/sessions'', json={''mode'': ''user''}\\); print\\(f''Status: {r.status_code}''\\); print\\(f''Response: {r.text}''\\)\")",
+      "Bash(start \"Atomizer Backend\" cmd /k C:UsersantoiAtomizerrestart_backend.bat)",
+      "Bash(start \"Test Backend\" cmd /c \"cd /d C:\\\\Users\\\\antoi\\\\Atomizer\\\\atomizer-dashboard\\\\backend && C:\\\\Users\\\\antoi\\\\anaconda3\\\\Scripts\\\\activate.bat atomizer && python -m uvicorn api.main:app --port 8002\")",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe C:UsersantoiAtomizertest_backend.py)",
+      "Bash(start \"Backend 8002\" C:UsersantoiAtomizerstart_backend_8002.bat)",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe -c \"from api.main import app; print\\(''Import OK''\\)\")",
+      "Bash(find:*)",
+      "Bash(npx tailwindcss:*)",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe -c \"from pathlib import Path; p = Path\\(''C:/Users/antoi/Atomizer/studies''\\) / ''M1_Mirror/m1_mirror_cost_reduction_lateral''; print\\(''exists:'', p.exists\\(\\), ''path:'', p\\)\")",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe -c \"import sys, json; d=json.load\\(sys.stdin\\); print\\(''Study:'', d.get\\(''meta'',{}\\).get\\(''study_name'',''N/A''\\)\\); print\\(''Design Variables:''\\); [print\\(f''  - {dv[\"\"name\"\"]} \\({dv[\"\"expression_name\"\"]}\\)''\\) for dv in d.get\\(''design_variables'',[]\\)]\")",
+      "Bash(C:Usersantoianaconda3envsatomizerpython.exe -m py_compile:*)",
+      "Skill(ralph-loop:ralph-loop)",
+      "Skill(ralph-loop:ralph-loop:*)",
+      "mcp__Claude_in_Chrome__computer",
+      "mcp__Claude_in_Chrome__navigate",
+      "Bash(/c/Users/antoi/anaconda3/envs/atomizer/python.exe -m pip install:*)",
+      "Bash(/c/Users/antoi/anaconda3/envs/atomizer/python.exe tests/compare_triangle_vs_gmsh.py)",
+      "Bash(/c/Users/antoi/anaconda3/envs/atomizer/python.exe:*)"
     ],
     "deny": [],
     "ask": []

.claude/skills/01_CHEATSHEET.md

@@ -1,7 +1,7 @@
 ---
 skill_id: SKILL_001
-version: 2.4
-last_updated: 2025-12-31
+version: 2.5
+last_updated: 2026-01-22
 type: reference
 code_dependencies:
   - optimization_engine/extractors/__init__.py
@@ -14,8 +14,8 @@ requires_skills:
 
 # Atomizer Quick Reference Cheatsheet
 
-**Version**: 2.4
-**Updated**: 2025-12-31
+**Version**: 2.5
+**Updated**: 2026-01-22
 **Purpose**: Rapid lookup for common operations. "I want X → Use Y"
 
 ---
@@ -37,6 +37,8 @@ requires_skills:
 | **Use SAT (Self-Aware Turbo)** | **SYS_16** | SAT v3 for high-efficiency neural-accelerated optimization |
 | Generate physics insight | SYS_17 | `python -m optimization_engine.insights generate <study>` |
 | **Manage knowledge/playbook** | **SYS_18** | `from optimization_engine.context import AtomizerPlaybook` |
+| **Automate dev tasks** | **DevLoop** | `python tools/devloop_cli.py start "task"` |
+| **Test dashboard UI** | **DevLoop** | `python tools/devloop_cli.py browser --level full` |
 
 ---
 
@@ -678,6 +680,67 @@ feedback.process_trial_result(
 
 ---
 
+## DevLoop Quick Reference
+
+Closed-loop development system using AI agents + Playwright testing.
+
+### CLI Commands
+
+| Task | Command |
+|------|---------|
+| Full dev cycle | `python tools/devloop_cli.py start "Create new study"` |
+| Plan only | `python tools/devloop_cli.py plan "Fix validation"` |
+| Implement plan | `python tools/devloop_cli.py implement` |
+| Test study files | `python tools/devloop_cli.py test --study support_arm` |
+| Analyze failures | `python tools/devloop_cli.py analyze` |
+| Browser smoke test | `python tools/devloop_cli.py browser` |
+| Browser full tests | `python tools/devloop_cli.py browser --level full` |
+| Check status | `python tools/devloop_cli.py status` |
+| Quick test | `python tools/devloop_cli.py quick` |
+
+### Browser Test Levels
+
+| Level | Description | Tests |
+|-------|-------------|-------|
+| `quick` | Smoke test (page loads) | 1 |
+| `home` | Home page verification | 2 |
+| `full` | All UI + study tests | 5+ |
+| `study` | Canvas/dashboard for specific study | 3 |
+
+### State Files (`.devloop/`)
+
+| File | Purpose |
+|------|---------|
+| `current_plan.json` | Current implementation plan |
+| `test_results.json` | Filesystem/API test results |
+| `browser_test_results.json` | Playwright test results |
+| `analysis.json` | Failure analysis |
+
+### Prerequisites
+
+```bash
+# Start backend
+cd atomizer-dashboard/backend && python -m uvicorn api.main:app --reload --port 8000
+
+# Start frontend
+cd atomizer-dashboard/frontend && npm run dev
+
+# Install Playwright (once)
+cd atomizer-dashboard/frontend && npx playwright install chromium
+```
+
+### Standalone Playwright Tests
+
+```bash
+cd atomizer-dashboard/frontend
+npm run test:e2e          # Run all E2E tests
+npm run test:e2e:ui       # Playwright UI mode
+```
+
+**Full documentation**: `docs/guides/DEVLOOP.md`
+
+---
+
 ## Report Generation Quick Reference (OP_08)
 
 Generate comprehensive study reports from optimization data.

.claude/skills/modules/study-readme-generator.md

@@ -0,0 +1,206 @@
+# Study README Generator Skill
+
+**Skill ID**: STUDY_README_GENERATOR  
+**Version**: 1.0  
+**Purpose**: Generate intelligent, context-aware README.md files for optimization studies
+
+## When to Use
+
+This skill is invoked automatically during the study intake workflow when:
+1. A study moves from `introspected` to `configured` status
+2. User explicitly requests README generation
+3. Finalizing a study from the inbox
+
+## Input Context
+
+The README generator receives:
+
+```json
+{
+  "study_name": "bracket_mass_opt_v1",
+  "topic": "Brackets",
+  "description": "User's description from intake form",
+  "spec": { /* Full AtomizerSpec v2.0 */ },
+  "introspection": {
+    "expressions": [...],
+    "mass_kg": 1.234,
+    "solver_type": "NX_Nastran"
+  },
+  "context_files": {
+    "goals.md": "User's goals markdown content",
+    "notes.txt": "Any additional notes"
+  }
+}
+```
+
+## Output Format
+
+Generate a README.md with these sections:
+
+### 1. Title & Overview
+```markdown
+# {Study Name}
+
+**Topic**: {Topic}  
+**Created**: {Date}  
+**Status**: {Status}
+
+{One paragraph executive summary of the optimization goal}
+```
+
+### 2. Engineering Problem
+```markdown
+## Engineering Problem
+
+{Describe the physical problem being solved}
+
+### Model Description
+- **Geometry**: {Describe the part/assembly}
+- **Material**: {If known from introspection}
+- **Baseline Mass**: {mass_kg} kg
+
+### Loading Conditions
+{Describe loads and boundary conditions if available}
+```
+
+### 3. Optimization Formulation
+```markdown
+## Optimization Formulation
+
+### Design Variables ({count})
+| Variable | Expression | Range | Units |
+|----------|------------|-------|-------|
+| {name} | {expr_name} | [{min}, {max}] | {units} |
+
+### Objectives ({count})
+| Objective | Direction | Weight | Source |
+|-----------|-----------|--------|--------|
+| {name} | {direction} | {weight} | {extractor} |
+
+### Constraints ({count})
+| Constraint | Condition | Threshold | Type |
+|------------|-----------|-----------|------|
+| {name} | {operator} | {threshold} | {type} |
+```
+
+### 4. Methodology
+```markdown
+## Methodology
+
+### Algorithm
+- **Primary**: {algorithm_type}
+- **Max Trials**: {max_trials}
+- **Surrogate**: {if enabled}
+
+### Physics Extraction
+{Describe extractors used}
+
+### Convergence Criteria
+{Describe stopping conditions}
+```
+
+### 5. Expected Outcomes
+```markdown
+## Expected Outcomes
+
+Based on the optimization setup:
+- Expected improvement: {estimate if baseline available}
+- Key trade-offs: {identify from objectives/constraints}
+- Risk factors: {any warnings from validation}
+```
+
+## Generation Guidelines
+
+1. **Be Specific**: Use actual values from the spec, not placeholders
+2. **Be Concise**: Engineers don't want to read novels
+3. **Be Accurate**: Only state facts that can be verified from input
+4. **Be Helpful**: Include insights that aid understanding
+5. **No Fluff**: Avoid marketing language or excessive praise
+
+## Claude Prompt Template
+
+```
+You are generating a README.md for an FEA optimization study. 
+
+CONTEXT:
+{json_context}
+
+RULES:
+1. Use the actual data provided - never use placeholder values
+2. Write in technical engineering language appropriate for structural engineers
+3. Keep each section concise but complete
+4. If information is missing, note it as "TBD" or skip the section
+5. Include physical units wherever applicable
+6. Format tables properly with alignment
+
+Generate the README.md content:
+```
+
+## Example Output
+
+```markdown
+# Bracket Mass Optimization V1
+
+**Topic**: Simple_Bracket  
+**Created**: 2026-01-22  
+**Status**: Configured
+
+Optimize the mass of a structural L-bracket while maintaining stress below yield and displacement within tolerance.
+
+## Engineering Problem
+
+### Model Description
+- **Geometry**: L-shaped mounting bracket with web and flange
+- **Material**: Steel (assumed based on typical applications)
+- **Baseline Mass**: 0.847 kg
+
+### Loading Conditions
+Static loading with force applied at mounting holes. Fixed constraints at base.
+
+## Optimization Formulation
+
+### Design Variables (3)
+| Variable | Expression | Range | Units |
+|----------|------------|-------|-------|
+| Web Thickness | web_thickness | [2.0, 10.0] | mm |
+| Flange Width | flange_width | [15.0, 40.0] | mm |
+| Fillet Radius | fillet_radius | [2.0, 8.0] | mm |
+
+### Objectives (1)
+| Objective | Direction | Weight | Source |
+|-----------|-----------|--------|--------|
+| Total Mass | minimize | 1.0 | mass_extractor |
+
+### Constraints (1)
+| Constraint | Condition | Threshold | Type |
+|------------|-----------|-----------|------|
+| Max Stress | <= | 250 MPa | hard |
+
+## Methodology
+
+### Algorithm
+- **Primary**: TPE (Tree-structured Parzen Estimator)
+- **Max Trials**: 100
+- **Surrogate**: Disabled
+
+### Physics Extraction
+- Mass: Extracted from NX expression `total_mass`
+- Stress: Von Mises stress from SOL101 static analysis
+
+### Convergence Criteria
+- Max trials: 100
+- Early stopping: 20 trials without improvement
+
+## Expected Outcomes
+
+Based on the optimization setup:
+- Expected improvement: 15-30% mass reduction (typical for thickness optimization)
+- Key trade-offs: Mass vs. stress margin
+- Risk factors: None identified
+```
+
+## Integration Points
+
+- **Backend**: `api/services/claude_readme.py` calls Claude API with this prompt
+- **Endpoint**: `POST /api/intake/{study_name}/readme`
+- **Trigger**: Automatic on status transition to `configured`

.devloop/browser_test_results.json

@@ -0,0 +1,33 @@
+{
+  "timestamp": "2026-01-22T18:13:30.884945",
+  "scenarios": [
+    {
+      "scenario_id": "browser_home_stats",
+      "scenario_name": "Home page shows statistics",
+      "passed": true,
+      "duration_ms": 1413.166,
+      "error": null,
+      "details": {
+        "navigated_to": "http://localhost:3003/",
+        "found_selector": "text=Total Trials"
+      }
+    },
+    {
+      "scenario_id": "browser_expand_folder",
+      "scenario_name": "Topic folder expands on click",
+      "passed": true,
+      "duration_ms": 2785.3219999999997,
+      "error": null,
+      "details": {
+        "navigated_to": "http://localhost:3003/",
+        "found_selector": "span:has-text('completed'), span:has-text('running'), span:has-text('paused')",
+        "clicked": "button:has-text('trials')"
+      }
+    }
+  ],
+  "summary": {
+    "passed": 2,
+    "failed": 0,
+    "total": 2
+  }
+}

.devloop/current_plan.json

@@ -0,0 +1,16 @@
+{
+  "objective": "Implement Dashboard Intake & AtomizerSpec Integration: Phase 1 - Create backend intake API routes (create, introspect, list, topics endpoints) and spec_manager service. The spec_models.py and JSON schema have already been updated with SpecStatus, IntrospectionData, BaselineData, and ExpressionInfo models. Now need to create: 1) backend/api/services/spec_manager.py for centralized spec CRUD, 2) backend/api/routes/intake.py with endpoints for creating inbox folders, running introspection, listing inbox contents, and listing topics, 3) Register the intake router in main.py. Reference the plan at docs/plans/DASHBOARD_INTAKE_ATOMIZERSPEC_INTEGRATION.md",
+  "approach": "Fallback plan - manual implementation",
+  "tasks": [
+    {
+      "id": "task_001",
+      "description": "Implement: Implement Dashboard Intake & AtomizerSpec Integration: Phase 1 - Create backend intake API routes (create, introspect, list, topics endpoints) and spec_manager service. The spec_models.py and JSON schema have already been updated with SpecStatus, IntrospectionData, BaselineData, and ExpressionInfo models. Now need to create: 1) backend/api/services/spec_manager.py for centralized spec CRUD, 2) backend/api/routes/intake.py with endpoints for creating inbox folders, running introspection, listing inbox contents, and listing topics, 3) Register the intake router in main.py. Reference the plan at docs/plans/DASHBOARD_INTAKE_ATOMIZERSPEC_INTEGRATION.md",
+      "file": "TBD",
+      "priority": "high"
+    }
+  ],
+  "test_scenarios": [],
+  "acceptance_criteria": [
+    "Implement Dashboard Intake & AtomizerSpec Integration: Phase 1 - Create backend intake API routes (create, introspect, list, topics endpoints) and spec_manager service. The spec_models.py and JSON schema have already been updated with SpecStatus, IntrospectionData, BaselineData, and ExpressionInfo models. Now need to create: 1) backend/api/services/spec_manager.py for centralized spec CRUD, 2) backend/api/routes/intake.py with endpoints for creating inbox folders, running introspection, listing inbox contents, and listing topics, 3) Register the intake router in main.py. Reference the plan at docs/plans/DASHBOARD_INTAKE_ATOMIZERSPEC_INTEGRATION.md"
+  ]
+}

.devloop/test_results.json

@@ -0,0 +1,64 @@
+{
+  "timestamp": "2026-01-22T21:10:54.742272",
+  "scenarios": [
+    {
+      "scenario_id": "test_study_dir",
+      "scenario_name": "Study directory exists: stage_3_arm",
+      "passed": true,
+      "duration_ms": 0.0,
+      "error": null,
+      "details": {
+        "path": "C:\\Users\\antoi\\Atomizer\\studies\\Stage3\\stage_3_arm",
+        "exists": true
+      }
+    },
+    {
+      "scenario_id": "test_spec",
+      "scenario_name": "AtomizerSpec is valid JSON",
+      "passed": true,
+      "duration_ms": 1.045,
+      "error": null,
+      "details": {
+        "valid_json": true
+      }
+    },
+    {
+      "scenario_id": "test_readme",
+      "scenario_name": "README exists",
+      "passed": true,
+      "duration_ms": 0.0,
+      "error": null,
+      "details": {
+        "path": "C:\\Users\\antoi\\Atomizer\\studies\\Stage3\\stage_3_arm\\README.md",
+        "exists": true
+      }
+    },
+    {
+      "scenario_id": "test_run_script",
+      "scenario_name": "run_optimization.py exists",
+      "passed": true,
+      "duration_ms": 0.0,
+      "error": null,
+      "details": {
+        "path": "C:\\Users\\antoi\\Atomizer\\studies\\Stage3\\stage_3_arm\\run_optimization.py",
+        "exists": true
+      }
+    },
+    {
+      "scenario_id": "test_model_dir",
+      "scenario_name": "Model directory exists",
+      "passed": true,
+      "duration_ms": 0.0,
+      "error": null,
+      "details": {
+        "path": "C:\\Users\\antoi\\Atomizer\\studies\\Stage3\\stage_3_arm\\1_setup\\model",
+        "exists": true
+      }
+    }
+  ],
+  "summary": {
+    "passed": 5,
+    "failed": 0,
+    "total": 5
+  }
+}

.gitignore

@@ -15,6 +15,11 @@ lib64/
 parts/
 sdist/
 var/
+
+# NOTE: This repo includes a React frontend that legitimately uses src/lib/.
+# The broad Python ignore `lib/` would ignore that. Re-include it:
+!atomizer-dashboard/frontend/src/lib/
+!atomizer-dashboard/frontend/src/lib/**
 wheels/
 *.egg-info/
 .installed.cfg
@@ -110,5 +115,30 @@ _dat_run*.dat
 .claude-mcp-*.json
 .claude-prompt-*.md
 
+# Backend logs
+backend_stdout.log
+backend_stderr.log
+*.log.bak
+
+# Linter/formatter caches
+.ruff_cache/
+.mypy_cache/
+
 # Auto-generated documentation (regenerate with: python -m optimization_engine.auto_doc all)
 docs/generated/
+
+# NX model introspection caches (generated)
+**/_introspection_*.json
+**/_introspection_cache.json
+**/_temp_introspection.json
+**/params.exp
+
+# Insight outputs (generated)
+**/3_insights/
+
+# Malformed filenames (Windows path used as filename)
+C:*
+*.gitmodules
+
+# project-context-sync (auto-generated, local only)
+PROJECT_STATE.md

.mcp.json

@@ -7,6 +7,10 @@
         "ATOMIZER_MODE": "user",
         "ATOMIZER_ROOT": "C:/Users/antoi/Atomizer"
       }
+    },
+    "nxopen-docs": {
+      "command": "C:/Users/antoi/CADtomaste/Atomaste-NXOpen-MCP/.venv/Scripts/python.exe",
+      "args": ["-m", "nxopen_mcp.server", "--data-dir", "C:/Users/antoi/CADtomaste/Atomaste-NXOpen-MCP/data"]
     }
   }
 }

.project-context.yml

@@ -0,0 +1,21 @@
+# project-context-sync configuration
+# See: https://github.com/clawdbot/skills/project-context-sync
+
+project_context:
+  # Use AI to generate smart summaries
+  # true: Rich context with inferred focus and suggestions (uses tokens)
+  # false: Raw git info only (fast, free)
+  ai_summary: true
+  
+  # How many recent commits to show
+  recent_commits: 5
+  
+  # Include file change stats in output
+  include_diff_stats: true
+  
+  # Sections to include in PROJECT_STATE.md
+  sections:
+    - last_commit       # Always included
+    - recent_changes    # Recent commit list
+    - current_focus     # AI-generated (requires ai_summary: true)
+    - suggested_next    # AI-generated (requires ai_summary: true)

CHANGELOG.md

@@ -6,6 +6,64 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ## [Unreleased]
 
+## [0.5.0] - 2025-01-24
+
+### Project Cleanup & Organization
+- Deleted 102+ orphaned MCP session temp files
+- Removed build artifacts (htmlcov, dist, __pycache__)
+- Archived superseded plan documents (RALPH_LOOP V2/V3, CANVAS V3, etc.)
+- Moved debug/analysis scripts from tests/ to tools/analysis/
+- Updated .gitignore with missing patterns
+- Cleaned empty directories
+
+## [0.4.0] - 2025-01-22
+
+### Canvas UX Improvements (Phases 7-9)
+- **Resizable Panels**: Left sidebar (200-400px) and right panel (280-600px) with localStorage persistence
+- **All Palette Items Enabled**: All 8 node types now draggable (model, solver, designVar, extractor, objective, constraint, algorithm, surrogate)
+- **Solver Configuration**: Engine selection (NX Nastran, MSC Nastran, Python Script) with solution type dropdowns (SOL101-SOL200)
+
+### AtomizerSpec v2.0
+- Unified JSON configuration schema for all studies
+- Added SolverEngine and NastranSolutionType types
+- Canvas position persistence for all nodes
+- Migration support from legacy optimization_config.json
+
+## [0.3.0] - 2025-01-18
+
+### Dashboard V3.1 - Canvas Builder
+- Visual workflow builder with 9 node types
+- Spec ↔ ReactFlow bidirectional converter
+- WebSocket real-time synchronization
+- Claude chat integration
+- Custom extractors with in-canvas code editor
+- Model introspection panel
+
+### Learning Atomizer Core (LAC)
+- Persistent memory system for accumulated knowledge
+- Session insights recording (failures, workarounds, patterns)
+- Optimization outcome tracking
+
+## [0.2.5] - 2025-01-16
+
+### GNN Surrogate for Zernike Optimization
+- PolarMirrorGraph with fixed 3000-node polar grid
+- ZernikeGNN model with design-conditioned convolutions
+- Differentiable GPU-accelerated Zernike fitting
+- Training pipeline with multi-task loss
+
+### DevLoop Automation
+- Closed-loop development system with AI agents
+- Gemini planning, Claude implementation
+- Playwright browser testing for dashboard UI
+
+## [0.2.1] - 2025-01-07
+
+### Optimization Engine v2.0 Restructure
+- Reorganized into modular subpackages (core/, nx/, study/, config/)
+- SpecManager for AtomizerSpec handling
+- Deprecation warnings for old import paths
+
 ### Phase 3.3 - Dashboard & Multi-Solution Support (November 23, 2025)
 
 #### Added

CLAUDE.md

@@ -55,6 +55,49 @@ If working directory is inside a study (`studies/*/`):
 - If no study context: Offer to create one or list available studies
 - After code changes: Update documentation proactively (SYS_12, cheatsheet)
 
+### Step 5: Use DevLoop for Multi-Step Development Tasks
+
+**CRITICAL: For any development task with 3+ steps, USE DEVLOOP instead of manual work.**
+
+DevLoop is the closed-loop development system that coordinates AI agents for autonomous development:
+
+```bash
+# Plan a task with Gemini
+python tools/devloop_cli.py plan "fix extractor exports"
+
+# Implement with Claude
+python tools/devloop_cli.py implement
+
+# Test filesystem/API
+python tools/devloop_cli.py test --study support_arm
+
+# Test dashboard UI with Playwright
+python tools/devloop_cli.py browser --level full
+
+# Analyze failures
+python tools/devloop_cli.py analyze
+
+# Full autonomous cycle
+python tools/devloop_cli.py start "add new stress extractor"
+```
+
+**When to use DevLoop:**
+- Fixing bugs that require multiple file changes
+- Adding new features or extractors
+- Debugging optimization failures
+- Testing dashboard UI changes
+- Any task that would take 3+ manual steps
+
+**Browser test levels:**
+- `quick` - Smoke test (1 test)
+- `home` - Home page verification (2 tests)
+- `full` - All UI tests (5+ tests)
+- `study` - Canvas/dashboard for specific study
+
+**DO NOT default to manual debugging** - use the automation we built!
+
+**Full documentation**: `docs/guides/DEVLOOP.md`
+
 ---
 
 ## Quick Start - Protocol Operating System

DEVELOPMENT.md

@@ -1,619 +0,0 @@
-# Atomizer Development Guide
-
-**Last Updated**: 2025-11-21
-**Current Phase**: Phase 3.2 - Integration Sprint + Documentation
-**Status**: 🟢 Core Complete (100%) | ✅ Protocols 10/11/13 Active (100%) | 🎯 Dashboard Live (95%) | 📚 Documentation Reorganized
-
-📘 **Quick Links**:
-- [Protocol Specifications](docs/PROTOCOLS.md) - All active protocols consolidated
-- [Documentation Index](docs/00_INDEX.md) - Complete documentation navigation
-- [README](README.md) - Project overview and quick start
-
----
-
-## Table of Contents
-
-1. [Current Phase](#current-phase)
-2. [Completed Features](#completed-features)
-3. [Active Development](#active-development)
-4. [Known Issues](#known-issues)
-5. [Testing Status](#testing-status)
-6. [Phase-by-Phase Progress](#phase-by-phase-progress)
-
----
-
-## Current Phase
-
-### Phase 3.2: Integration Sprint (🎯 TOP PRIORITY)
-
-**Goal**: Connect LLM intelligence components to production workflow
-
-**Timeline**: 2-4 weeks (Started 2025-11-17)
-
-**Status**: LLM components built and tested individually (85% complete). Need to wire them into production runner.
-
-📋 **Detailed Plan**: [docs/PHASE_3_2_INTEGRATION_PLAN.md](docs/PHASE_3_2_INTEGRATION_PLAN.md)
-
-**Critical Path**:
-
-#### Week 1: Make LLM Mode Accessible (16 hours)
-- [ ] **1.1** Create unified entry point `optimization_engine/run_optimization.py` (4h)
-  - Add `--llm` flag for natural language mode
-  - Add `--request` parameter for natural language input
-  - Support both LLM and traditional JSON modes
-  - Preserve backward compatibility
-
-- [ ] **1.2** Wire LLMOptimizationRunner to production (8h)
-  - Connect LLMWorkflowAnalyzer to entry point
-  - Bridge LLMOptimizationRunner → OptimizationRunner
-  - Pass model updater and simulation runner callables
-  - Integrate with existing hook system
-
-- [ ] **1.3** Create minimal example (2h)
-  - Create `examples/llm_mode_demo.py`
-  - Show natural language → optimization results
-  - Compare traditional (100 lines) vs LLM (3 lines)
-
-- [ ] **1.4** End-to-end integration test (2h)
-  - Test with simple_beam_optimization study
-  - Verify extractors generated correctly
-  - Validate output matches manual mode
-
-#### Week 2: Robustness & Safety (16 hours)
-- [ ] **2.1** Code validation pipeline (6h)
-  - Create `optimization_engine/code_validator.py`
-  - Implement syntax validation (ast.parse)
-  - Implement security scanning (whitelist imports)
-  - Implement test execution on example OP2
-  - Add retry with LLM feedback on failure
-
-- [ ] **2.2** Graceful fallback mechanisms (4h)
-  - Wrap all LLM calls in try/except
-  - Provide clear error messages
-  - Offer fallback to manual mode
-  - Never crash on LLM failure
-
-- [ ] **2.3** LLM audit trail (3h)
-  - Create `optimization_engine/llm_audit.py`
-  - Log all LLM requests and responses
-  - Log generated code with prompts
-  - Create `llm_audit.json` in study output
-
-- [ ] **2.4** Failure scenario testing (3h)
-  - Test invalid natural language request
-  - Test LLM unavailable
-  - Test generated code syntax errors
-  - Test validation failures
-
-#### Week 3: Learning System (12 hours)
-- [ ] **3.1** Knowledge base implementation (4h)
-  - Create `optimization_engine/knowledge_base.py`
-  - Implement `save_session()` - Save successful workflows
-  - Implement `search_templates()` - Find similar patterns
-  - Add confidence scoring
-
-- [ ] **3.2** Template extraction (4h)
-  - Extract reusable patterns from generated code
-  - Parameterize variable parts
-  - Save templates with usage examples
-  - Implement template application to new requests
-
-- [ ] **3.3** ResearchAgent integration (4h)
-  - Complete ResearchAgent implementation
-  - Integrate into ExtractorOrchestrator error handling
-  - Add user example collection workflow
-  - Save learned knowledge to knowledge base
-
-#### Week 4: Documentation & Discoverability (8 hours)
-- [ ] **4.1** Update README (2h)
-  - Add "🤖 LLM-Powered Mode" section
-  - Show example command with natural language
-  - Link to detailed docs
-
-- [ ] **4.2** Create LLM mode documentation (3h)
-  - Create `docs/LLM_MODE.md`
-  - Explain how LLM mode works
-  - Provide usage examples
-  - Add troubleshooting guide
-
-- [ ] **4.3** Create demo video/GIF (1h)
-  - Record terminal session
-  - Show before/after (100 lines → 3 lines)
-  - Create animated GIF for README
-
-- [ ] **4.4** Update all planning docs (2h)
-  - Update DEVELOPMENT.md status
-  - Update DEVELOPMENT_GUIDANCE.md (80-90% → 90-95%)
-  - Mark Phase 3.2 as ✅ Complete
-
----
-
-## Completed Features
-
-### ✅ Live Dashboard System (Completed 2025-11-21)
-
-#### Backend (FastAPI + WebSocket)
-- [x] **FastAPI Backend** ([atomizer-dashboard/backend/](atomizer-dashboard/backend/))
-  - REST API endpoints for study management
-  - WebSocket streaming with file watching (Watchdog)
-  - Real-time updates (<100ms latency)
-  - CORS configured for local development
-
-- [x] **REST API Endpoints** ([backend/api/routes/optimization.py](atomizer-dashboard/backend/api/routes/optimization.py))
-  - `GET /api/optimization/studies` - List all studies
-  - `GET /api/optimization/studies/{id}/status` - Get study status
-  - `GET /api/optimization/studies/{id}/history` - Get trial history
-  - `GET /api/optimization/studies/{id}/pruning` - Get pruning diagnostics
-
-- [x] **WebSocket Streaming** ([backend/api/websocket/optimization_stream.py](atomizer-dashboard/backend/api/websocket/optimization_stream.py))
-  - File watching on `optimization_history_incremental.json`
-  - Real-time trial updates via WebSocket
-  - Pruning alerts and progress updates
-  - Automatic observer lifecycle management
-
-#### Frontend (HTML + Chart.js)
-- [x] **Enhanced Live Dashboard** ([atomizer-dashboard/dashboard-enhanced.html](atomizer-dashboard/dashboard-enhanced.html))
-  - Real-time WebSocket updates
-  - Interactive convergence chart (Chart.js)
-  - Parameter space scatter plot
-  - Pruning alerts (toast notifications)
-  - Data export (JSON/CSV)
-  - Study auto-discovery and selection
-  - Metric dashboard (trials, best value, pruned count)
-
-#### React Frontend (In Progress)
-- [x] **Project Configuration** ([atomizer-dashboard/frontend/](atomizer-dashboard/frontend/))
-  - React 18 + Vite 5 + TypeScript 5.2
-  - TailwindCSS 3.3 for styling
-  - Recharts 2.10 for charts
-  - Complete build configuration
-
-- [x] **TypeScript Types** ([frontend/src/types/](atomizer-dashboard/frontend/src/types/))
-  - Complete type definitions for API data
-  - WebSocket message types
-  - Chart data structures
-
-- [x] **Custom Hooks** ([frontend/src/hooks/useWebSocket.ts](atomizer-dashboard/frontend/src/hooks/useWebSocket.ts))
-  - WebSocket connection management
-  - Auto-reconnection with exponential backoff
-  - Type-safe message routing
-
-- [x] **Reusable Components** ([frontend/src/components/](atomizer-dashboard/frontend/src/components/))
-  - Card, MetricCard, Badge, StudyCard components
-  - TailwindCSS styling with dark theme
-
-- [ ] **Dashboard Page** (Pending manual completion)
-  - Need to run `npm install`
-  - Create main.tsx, App.tsx, Dashboard.tsx
-  - Integrate Recharts for charts
-  - Test end-to-end
-
-#### Documentation
-- [x] **Dashboard Master Plan** ([docs/DASHBOARD_MASTER_PLAN.md](docs/DASHBOARD_MASTER_PLAN.md))
-  - Complete 3-page architecture (Configurator, Live Dashboard, Results Viewer)
-  - Tech stack recommendations
-  - Implementation phases
-
-- [x] **Implementation Status** ([docs/DASHBOARD_IMPLEMENTATION_STATUS.md](docs/DASHBOARD_IMPLEMENTATION_STATUS.md))
-  - Current progress tracking
-  - Testing instructions
-  - Next steps
-
-- [x] **React Implementation Guide** ([docs/DASHBOARD_REACT_IMPLEMENTATION.md](docs/DASHBOARD_REACT_IMPLEMENTATION.md))
-  - Complete templates for remaining components
-  - Recharts integration examples
-  - Troubleshooting guide
-
-- [x] **Session Summary** ([docs/DASHBOARD_SESSION_SUMMARY.md](docs/DASHBOARD_SESSION_SUMMARY.md))
-  - Features demonstrated
-  - How to use the dashboard
-  - Architecture explanation
-
-### ✅ Phase 1: Plugin System & Infrastructure (Completed 2025-01-16)
-
-#### Core Architecture
-- [x] **Hook Manager** ([optimization_engine/plugins/hook_manager.py](optimization_engine/plugins/hook_manager.py))
-  - Hook registration with priority-based execution
-  - Auto-discovery from plugin directories
-  - Context passing to all hooks
-  - Execution history tracking
-
-- [x] **Lifecycle Hooks**
-  - `pre_solve`: Execute before solver launch
-  - `post_solve`: Execute after solve, before extraction
-  - `post_extraction`: Execute after result extraction
-
-#### Logging Infrastructure
-- [x] **Detailed Trial Logs** ([detailed_logger.py](optimization_engine/plugins/pre_solve/detailed_logger.py))
-  - Per-trial log files in `optimization_results/trial_logs/`
-  - Complete iteration trace with timestamps
-  - Design variables, configuration, timeline
-  - Extracted results and constraint evaluations
-
-- [x] **High-Level Optimization Log** ([optimization_logger.py](optimization_engine/plugins/pre_solve/optimization_logger.py))
-  - `optimization.log` file tracking overall progress
-  - Configuration summary header
-  - Compact START/COMPLETE entries per trial
-  - Easy to scan format for monitoring
-
-- [x] **Result Appenders**
-  - [log_solve_complete.py](optimization_engine/plugins/post_solve/log_solve_complete.py) - Appends solve completion to trial logs
-  - [log_results.py](optimization_engine/plugins/post_extraction/log_results.py) - Appends extracted results to trial logs
-  - [optimization_logger_results.py](optimization_engine/plugins/post_extraction/optimization_logger_results.py) - Appends results to optimization.log
-
-#### Project Organization
-- [x] **Studies Structure** ([studies/](studies/))
-  - Standardized folder layout with `model/`, `optimization_results/`, `analysis/`
-  - Comprehensive documentation in [studies/README.md](studies/README.md)
-  - Example study: [bracket_stress_minimization/](studies/bracket_stress_minimization/)
-  - Template structure for future studies
-
-- [x] **Path Resolution** ([atomizer_paths.py](atomizer_paths.py))
-  - Intelligent project root detection using marker files
-  - Helper functions: `root()`, `optimization_engine()`, `studies()`, `tests()`
-  - `ensure_imports()` for robust module imports
-  - Works regardless of script location
-
-#### Testing
-- [x] **Hook Validation Test** ([test_hooks_with_bracket.py](tests/test_hooks_with_bracket.py))
-  - Verifies hook loading and execution
-  - Tests 3 trials with dummy data
-  - Checks hook execution history
-
-- [x] **Integration Tests**
-  - [run_5trial_test.py](tests/run_5trial_test.py) - Quick 5-trial optimization
-  - [test_journal_optimization.py](tests/test_journal_optimization.py) - Full optimization test
-
-#### Runner Enhancements
-- [x] **Context Passing** ([runner.py:332,365,412](optimization_engine/runner.py))
-  - `output_dir` passed to all hook contexts
-  - Trial number, design variables, extracted results
-  - Configuration dictionary available to hooks
-
-### ✅ Core Engine (Pre-Phase 1)
-- [x] Optuna integration with TPE sampler
-- [x] Multi-objective optimization support
-- [x] NX journal execution ([nx_solver.py](optimization_engine/nx_solver.py))
-- [x] Expression updates ([nx_updater.py](optimization_engine/nx_updater.py))
-- [x] OP2 result extraction (stress, displacement)
-- [x] Study management with resume capability
-- [x] Web dashboard (real-time monitoring)
-- [x] Precision control (4-decimal rounding)
-
----
-
-## Active Development
-
-### In Progress - Dashboard (High Priority)
-- [x] Backend API complete (FastAPI + WebSocket)
-- [x] HTML dashboard with Chart.js complete
-- [x] React project structure and configuration complete
-- [ ] **Complete React frontend** (Awaiting manual npm install)
-  - [ ] Run `npm install` in frontend directory
-  - [ ] Create main.tsx and App.tsx
-  - [ ] Create Dashboard.tsx with Recharts
-  - [ ] Test end-to-end with live optimization
-
-### Up Next - Dashboard (Next Session)
-- [ ] Study Configurator page (React)
-- [ ] Results Report Viewer page (React)
-- [ ] LLM chat interface integration (future)
-- [ ] Docker deployment configuration
-
-### In Progress - Phase 3.2 Integration
-- [ ] Feature registry creation (Phase 2, Week 1)
-- [ ] Claude skill definition (Phase 2, Week 1)
-
-### Up Next (Phase 2, Week 2)
-- [ ] Natural language parser
-- [ ] Intent classification system
-- [ ] Entity extraction for optimization parameters
-- [ ] Conversational workflow manager
-
-### Backlog (Phase 3+)
-- [ ] Custom function generator (RSS, weighted objectives)
-- [ ] Journal script generator
-- [ ] Code validation pipeline
-- [ ] Result analyzer with statistical analysis
-- [ ] Surrogate quality checker
-- [ ] HTML/PDF report generator
-
----
-
-## Known Issues
-
-### Critical
-- None currently
-
-### Minor
-- [ ] `.claude/settings.local.json` modified during development (contains user-specific settings)
-- [ ] Some old bash background processes still running from previous tests
-
-### Documentation
-- [ ] Need to add examples of custom hooks to studies/README.md
-- [ ] Missing API documentation for hook_manager methods
-- [ ] No developer guide for creating new plugins
-
----
-
-## Testing Status
-
-### Automated Tests
-- ✅ **Hook system** - `test_hooks_with_bracket.py` passing
-- ✅ **5-trial integration** - `run_5trial_test.py` working
-- ✅ **Full optimization** - `test_journal_optimization.py` functional
-- ⏳ **Unit tests** - Need to create for individual modules
-- ⏳ **CI/CD pipeline** - Not yet set up
-
-### Manual Testing
-- ✅ Bracket optimization (50 trials)
-- ✅ Log file generation in correct locations
-- ✅ Hook execution at all lifecycle points
-- ✅ Path resolution across different script locations
-- ✅ **Dashboard backend** - REST API and WebSocket tested successfully
-- ✅ **HTML dashboard** - Live updates working with Chart.js
-- ⏳ **React dashboard** - Pending npm install and completion
-- ⏳ Resume functionality with config validation
-
-### Test Coverage
-- Hook manager: ~80% (core functionality tested)
-- Logging plugins: 100% (tested via integration tests)
-- Path resolution: 100% (tested in all scripts)
-- Result extractors: ~70% (basic tests exist)
-- **Dashboard backend**: ~90% (REST endpoints and WebSocket tested)
-- **Dashboard frontend**: ~60% (HTML version tested, React pending)
-- Overall: ~65% estimated
-
----
-
-## Phase-by-Phase Progress
-
-### Phase 1: Plugin System ✅ (100% Complete)
-
-**Completed** (2025-01-16):
-- [x] Hook system for optimization lifecycle
-- [x] Plugin auto-discovery and registration
-- [x] Hook manager with priority-based execution
-- [x] Detailed per-trial logs (`trial_logs/`)
-- [x] High-level optimization log (`optimization.log`)
-- [x] Context passing system for hooks
-- [x] Studies folder structure
-- [x] Comprehensive studies documentation
-- [x] Model file organization (`model/` folder)
-- [x] Intelligent path resolution
-- [x] Test suite for hook system
-
-**Deferred to Future Phases**:
-- Feature registry → Phase 2 (with LLM interface)
-- `pre_mesh` and `post_mesh` hooks → Future (not needed for current workflow)
-- Custom objective/constraint registration → Phase 3 (Code Generation)
-
----
-
-### Phase 2: LLM Integration 🟡 (0% Complete)
-
-**Target**: 2 weeks (Started 2025-01-16)
-
-#### Week 1 Todos (Feature Registry & Claude Skill)
-- [ ] Create `optimization_engine/feature_registry.json`
-- [ ] Extract all current capabilities
-- [ ] Draft `.claude/skills/atomizer.md`
-- [ ] Test LLM's ability to navigate codebase
-
-#### Week 2 Todos (Natural Language Interface)
-- [ ] Implement intent classifier
-- [ ] Build entity extractor
-- [ ] Create workflow manager
-- [ ] Test end-to-end: "Create a stress minimization study"
-
-**Success Criteria**:
-- [ ] LLM can create optimization from natural language in <5 turns
-- [ ] 90% of user requests understood correctly
-- [ ] Zero manual JSON editing required
-
----
-
-### Phase 3: Code Generation ⏳ (Not Started)
-
-**Target**: 3 weeks
-
-**Key Deliverables**:
-- [ ] Custom function generator
-  - [ ] RSS (Root Sum Square) template
-  - [ ] Weighted objectives template
-  - [ ] Custom constraints template
-- [ ] Journal script generator
-- [ ] Code validation pipeline
-- [ ] Safe execution environment
-
-**Success Criteria**:
-- [ ] LLM generates 10+ custom functions with zero errors
-- [ ] All generated code passes safety validation
-- [ ] Users save 50% time vs. manual coding
-
----
-
-### Phase 4: Analysis & Decision Support ⏳ (Not Started)
-
-**Target**: 3 weeks
-
-**Key Deliverables**:
-- [ ] Result analyzer (convergence, sensitivity, outliers)
-- [ ] Surrogate model quality checker (R², CV score, confidence intervals)
-- [ ] Decision assistant (trade-offs, what-if analysis, recommendations)
-
-**Success Criteria**:
-- [ ] Surrogate quality detection 95% accurate
-- [ ] Recommendations lead to 30% faster convergence
-- [ ] Users report higher confidence in results
-
----
-
-### Phase 5: Automated Reporting ⏳ (Not Started)
-
-**Target**: 2 weeks
-
-**Key Deliverables**:
-- [ ] Report generator with Jinja2 templates
-- [ ] Multi-format export (HTML, PDF, Markdown, JSON)
-- [ ] LLM-written narrative explanations
-
-**Success Criteria**:
-- [ ] Reports generated in <30 seconds
-- [ ] Narrative quality rated 4/5 by engineers
-- [ ] 80% of reports used without manual editing
-
----
-
-### Phase 6: NX MCP Enhancement ⏳ (Not Started)
-
-**Target**: 4 weeks
-
-**Key Deliverables**:
-- [ ] NX documentation MCP server
-- [ ] Advanced NX operations library
-- [ ] Feature bank with 50+ pre-built operations
-
-**Success Criteria**:
-- [ ] NX MCP answers 95% of API questions correctly
-- [ ] Feature bank covers 80% of common workflows
-- [ ] Users write 50% less manual journal code
-
----
-
-### Phase 7: Self-Improving System ⏳ (Not Started)
-
-**Target**: 4 weeks
-
-**Key Deliverables**:
-- [ ] Feature learning system
-- [ ] Best practices database
-- [ ] Continuous documentation generation
-
-**Success Criteria**:
-- [ ] 20+ user-contributed features in library
-- [ ] Pattern recognition identifies 10+ best practices
-- [ ] Documentation auto-updates with zero manual effort
-
----
-
-## Development Commands
-
-### Running Dashboard
-```bash
-# Start backend server
-cd atomizer-dashboard/backend
-python -m uvicorn api.main:app --reload --host 0.0.0.0 --port 8000
-
-# Access HTML dashboard (current)
-# Open browser: http://localhost:8000
-
-# Start React frontend (when ready)
-cd atomizer-dashboard/frontend
-npm install  # First time only
-npm run dev  # Starts on http://localhost:3000
-```
-
-### Running Tests
-```bash
-# Hook validation (3 trials, fast)
-python tests/test_hooks_with_bracket.py
-
-# Quick integration test (5 trials)
-python tests/run_5trial_test.py
-
-# Full optimization test
-python tests/test_journal_optimization.py
-```
-
-### Code Quality
-```bash
-# Run linter (when available)
-# pylint optimization_engine/
-
-# Run type checker (when available)
-# mypy optimization_engine/
-
-# Run all tests (when test suite is complete)
-# pytest tests/
-```
-
-### Git Workflow
-```bash
-# Stage all changes
-git add .
-
-# Commit with conventional commits format
-git commit -m "feat: description"  # New feature
-git commit -m "fix: description"   # Bug fix
-git commit -m "docs: description"  # Documentation
-git commit -m "test: description"  # Tests
-git commit -m "refactor: description"  # Code refactoring
-
-# Push to GitHub
-git push origin main
-```
-
----
-
-## Documentation
-
-### For Developers
-- [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md) - Strategic vision and phases
-- [studies/README.md](studies/README.md) - Studies folder organization
-- [CHANGELOG.md](CHANGELOG.md) - Version history
-
-### Dashboard Documentation
-- [docs/DASHBOARD_MASTER_PLAN.md](docs/DASHBOARD_MASTER_PLAN.md) - Complete architecture blueprint
-- [docs/DASHBOARD_IMPLEMENTATION_STATUS.md](docs/DASHBOARD_IMPLEMENTATION_STATUS.md) - Current progress
-- [docs/DASHBOARD_REACT_IMPLEMENTATION.md](docs/DASHBOARD_REACT_IMPLEMENTATION.md) - React implementation guide
-- [docs/DASHBOARD_SESSION_SUMMARY.md](docs/DASHBOARD_SESSION_SUMMARY.md) - Session summary
-- [atomizer-dashboard/README.md](atomizer-dashboard/README.md) - Dashboard quick start
-- [atomizer-dashboard/backend/README.md](atomizer-dashboard/backend/README.md) - Backend API docs
-- [atomizer-dashboard/frontend/README.md](atomizer-dashboard/frontend/README.md) - Frontend setup guide
-
-### For Users
-- [README.md](README.md) - Project overview and quick start
-- [docs/INDEX.md](docs/INDEX.md) - Complete documentation index
-- [docs/](docs/) - Additional documentation
-
----
-
-## Notes
-
-### Architecture Decisions
-- **Hook system**: Chose priority-based execution to allow precise control of plugin order
-- **Path resolution**: Used marker files instead of environment variables for simplicity
-- **Logging**: Two-tier system (detailed trial logs + high-level optimization.log) for different use cases
-
-### Performance Considerations
-- Hook execution adds <1s overhead per trial (acceptable for FEA simulations)
-- Path resolution caching could improve startup time (future optimization)
-- Log file sizes grow linearly with trials (~10KB per trial)
-
-### Future Considerations
-- Consider moving to structured logging (JSON) for easier parsing
-- May need database for storing hook execution history (currently in-memory)
-- Dashboard integration will require WebSocket for real-time log streaming
-
----
-
-**Last Updated**: 2025-11-21
-**Maintained by**: Antoine Polvé (antoine@atomaste.com)
-**Repository**: [GitHub - Atomizer](https://github.com/yourusername/Atomizer)
-
----
-
-## Recent Updates (November 21, 2025)
-
-### Dashboard System Implementation ✅
-- **Backend**: FastAPI + WebSocket with real-time file watching complete
-- **HTML Dashboard**: Functional dashboard with Chart.js, data export, pruning alerts
-- **React Setup**: Complete project configuration, types, hooks, components
-- **Documentation**: 5 comprehensive markdown documents covering architecture, implementation, and usage
-
-### Next Immediate Steps
-1. Run `npm install` in `atomizer-dashboard/frontend`
-2. Create `main.tsx`, `App.tsx`, and `Dashboard.tsx` using provided templates
-3. Test React dashboard with live optimization
-4. Build Study Configurator page (next major feature)

INSTALL_INSTRUCTIONS.md

@@ -1,63 +0,0 @@
-# Atomizer Installation Guide
-
-## Step 1: Install Miniconda (Recommended)
-
-1. Download Miniconda from: https://docs.conda.io/en/latest/miniconda.html
-   - Choose: **Miniconda3 Windows 64-bit**
-
-2. Run the installer:
-   - Check "Add Miniconda3 to my PATH environment variable"
-   - Check "Register Miniconda3 as my default Python"
-
-3. Restart your terminal/VSCode after installation
-
-## Step 2: Create Atomizer Environment
-
-Open **Anaconda Prompt** (or any terminal after restart) and run:
-
-```bash
-cd C:\Users\Antoine\Atomizer
-conda env create -f environment.yml
-conda activate atomizer
-```
-
-## Step 3: Install PyTorch with GPU Support (Optional but Recommended)
-
-If you have an NVIDIA GPU:
-
-```bash
-conda activate atomizer
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-pip install torch-geometric
-```
-
-## Step 4: Verify Installation
-
-```bash
-conda activate atomizer
-python -c "import torch; import optuna; import pyNastran; print('All imports OK!')"
-python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
-```
-
-## Step 5: Train Neural Network
-
-```bash
-conda activate atomizer
-cd C:\Users\Antoine\Atomizer\atomizer-field
-python train_parametric.py --train_dir ../atomizer_field_training_data/bracket_stiffness_optimization_atomizerfield --epochs 100 --output_dir runs/bracket_model
-```
-
-## Quick Commands Reference
-
-```bash
-# Activate environment (do this every time you open a new terminal)
-conda activate atomizer
-
-# Train neural network
-cd C:\Users\Antoine\Atomizer\atomizer-field
-python train_parametric.py --train_dir ../atomizer_field_training_data/bracket_stiffness_optimization_atomizerfield --epochs 100
-
-# Run optimization with neural acceleration
-cd C:\Users\Antoine\Atomizer\studies\bracket_stiffness_optimization_atomizerfield
-python run_optimization.py --run --trials 100 --enable-nn
-```

PROJECT_STATUS.md

@@ -0,0 +1,111 @@
+# PROJECT_STATUS.md
+
+> **Bridge document for Mario (Clawdbot) ↔ Claude Code coordination**
+> 
+> Both AIs should read this at session start. Update when priorities change.
+
+*Last updated: 2026-01-27 by Mario*
+
+---
+
+## Current Focus
+
+**Phase**: Foundation (Phase 1)
+**Sprint**: 2026-01-27 to 2026-02-03
+
+### This Week's Priorities
+
+**Now (Sprint 1.5): Draft + Publish (S2)**
+1. 🔴 Implement DraftManager (local autosave draft per study)
+2. 🔴 Add Draft vs Published banner + Publish button
+3. 🔴 Restore/discard draft prompt on load
+
+**Next (Sprint 2): Create Wizard v1 shell**
+4. 🟡 /create route + stepper
+5. 🟡 Files step (dependency tree + _i.prt warnings)
+6. 🟡 Introspection step (expressions + DV selection)
+
+### Completed recently
+- Spec/Canvas wiring sync foundation (converters, connect/delete wiring, output picker, panel rewiring, edge projection)
+
+### Blocked
+- None (but local npm install on this server fails due to peer deps; run builds/tests on Windows dev env)
+
+---
+
+## Active Decisions
+
+| Decision | Summary | Date |
+|----------|---------|------|
+| Full Partnership | Mario = PM, reviewer, architect. Antoine = developer, NX. | 2026-01-27 |
+| Dashboard on Windows | Keep simple for now, hybrid architecture later | 2026-01-27 |
+| Adopt Clawdbot Patterns | MEMORY.md, QUICK_REF.md, simplified CLAUDE.md | 2026-01-27 |
+
+---
+
+## For Claude Code
+
+When starting a session:
+
+1. ✅ Read CLAUDE.md (system instructions)
+2. ✅ Read PROJECT_STATUS.md (this file — current priorities)
+3. ✅ Read `knowledge_base/lac/session_insights/failure.jsonl` (critical lessons)
+4. 🔲 After session: Commit any new LAC insights to Git
+
+### LAC Commit Protocol (NEW)
+
+After each significant session, commit LAC changes:
+
+```bash
+cd Atomizer
+git add knowledge_base/lac/
+git commit -m "lac: Session insights from YYYY-MM-DD"
+git push origin main && git push github main
+```
+
+This ensures Mario can see what Claude Code learned.
+
+---
+
+## For Mario (Clawdbot)
+
+When checking on Atomizer:
+
+1. Pull latest from Gitea: `cd /home/papa/repos/Atomizer && git pull`
+2. Check `knowledge_base/lac/session_insights/` for new learnings
+3. Update tracking files in `/home/papa/clawd/memory/atomizer/`
+4. Update this file if priorities change
+
+### Heartbeat Check (Add to HEARTBEAT.md)
+
+```markdown
+### Atomizer Check (weekly)
+- git pull Atomizer repo
+- Check for new LAC insights
+- Review recent commits
+- Update roadmap if needed
+```
+
+---
+
+## Recent Activity
+
+| Date | Activity | Who |
+|------|----------|-----|
+| 2026-01-27 | Created master plan in PKM | Mario |
+| 2026-01-27 | Created tracking files | Mario |
+| 2026-01-27 | ACKed Atomizer project | Mario |
+| 2026-01-27 | Canvas V3.1 improvements | Claude Code (prior) |
+
+---
+
+## Links
+
+- **Master Plan**: `/home/papa/obsidian-vault/2-Projects/Atomizer-AtomasteAI/Development/ATOMIZER-NEXT-LEVEL-MASTERPLAN.md`
+- **Mario's Tracking**: `/home/papa/clawd/memory/atomizer/`
+- **LAC Insights**: `knowledge_base/lac/session_insights/`
+- **Full Roadmap**: See Master Plan in PKM
+
+---
+
+*This file lives in the repo. Both AIs can read it. Only update when priorities change.*

atomizer-dashboard/backend/api/main.py

@@ -13,7 +13,19 @@ import sys
 # Add parent directory to path to import optimization_engine
 sys.path.append(str(Path(__file__).parent.parent.parent.parent))
 
-from api.routes import optimization, claude, terminal, insights, context, files, nx, claude_code, spec
+from api.routes import (
+    optimization,
+    claude,
+    terminal,
+    insights,
+    context,
+    files,
+    nx,
+    claude_code,
+    spec,
+    devloop,
+    intake,
+)
 from api.websocket import optimization_stream
 
 
@@ -23,6 +35,7 @@ async def lifespan(app: FastAPI):
     """Manage application lifespan - start/stop session manager"""
     # Startup
     from api.routes.claude import get_session_manager
+
     manager = get_session_manager()
     await manager.start()
     print("Session manager started")
@@ -63,6 +76,9 @@ app.include_router(nx.router, prefix="/api/nx", tags=["nx"])
 app.include_router(claude_code.router, prefix="/api", tags=["claude-code"])
 app.include_router(spec.router, prefix="/api", tags=["spec"])
 app.include_router(spec.validate_router, prefix="/api", tags=["spec"])
+app.include_router(devloop.router, prefix="/api", tags=["devloop"])
+app.include_router(intake.router, prefix="/api", tags=["intake"])
+
 
 @app.get("/")
 async def root():
@@ -70,11 +86,13 @@ async def root():
     dashboard_path = Path(__file__).parent.parent.parent / "dashboard-enhanced.html"
     return FileResponse(dashboard_path)
 
+
 @app.get("/health")
 async def health_check():
     """Health check endpoint with database status"""
     try:
         from api.services.conversation_store import ConversationStore
+
         store = ConversationStore()
         # Test database by creating/getting a health check session
         store.get_session("health_check")
@@ -87,12 +105,8 @@ async def health_check():
         "database": db_status,
     }
 
+
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(
-        "main:app",
-        host="0.0.0.0",
-        port=8000,
-        reload=True,
-        log_level="info"
-    )
+
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True, log_level="info")

atomizer-dashboard/backend/api/routes/claude_code.py

@@ -83,23 +83,49 @@ async def generate_extractor_code(request: ExtractorGenerationRequest):
     # Build focused system prompt for extractor generation
     system_prompt = """You are generating a Python custom extractor function for Atomizer FEA optimization.
 
-The function MUST:
-1. Have signature: def extract(op2_path: str, fem_path: str, params: dict, subcase_id: int = 1) -> dict
-2. Return a dict with extracted values (e.g., {"max_stress": 150.5, "mass": 2.3})
-3. Use pyNastran.op2.op2.OP2 for reading OP2 results
-4. Handle missing data gracefully with try/except blocks
+IMPORTANT: Choose the appropriate function signature based on what data is needed:
 
-Available imports (already available, just use them):
-- from pyNastran.op2.op2 import OP2
-- import numpy as np
-- from pathlib import Path
+## Option 1: FEA Results (OP2) - Use for stresses, displacements, frequencies, forces
+```python
+def extract(op2_path: str, fem_path: str, params: dict, subcase_id: int = 1) -> dict:
+    from pyNastran.op2.op2 import OP2
+    op2 = OP2()
+    op2.read_op2(op2_path)
+    # Access: op2.displacements[subcase_id], op2.cquad4_stress[subcase_id], etc.
+    return {"max_stress": value}
+```
 
-Common patterns:
-- Displacement: op2.displacements[subcase_id].data[0, :, 1:4] (x,y,z components)
+## Option 2: Expression/Computed Values (no FEA needed) - Use for dimensions, volumes, derived values
+```python
+def extract(trial_dir: str, config: dict, context: dict) -> dict:
+    import json
+    from pathlib import Path
+    
+    # Read mass properties (if available from model introspection)
+    mass_file = Path(trial_dir) / "mass_properties.json"
+    if mass_file.exists():
+        with open(mass_file) as f:
+            props = json.load(f)
+        mass = props.get("mass_kg", 0)
+    
+    # Or use config values directly (e.g., expression values)
+    length_mm = config.get("length_expression", 100)
+    
+    # context has results from other extractors
+    other_value = context.get("other_extractor_output", 0)
+    
+    return {"computed_value": length_mm * 2}
+```
+
+Available imports: pyNastran.op2.op2.OP2, numpy, pathlib.Path, json
+
+Common OP2 patterns:
+- Displacement: op2.displacements[subcase_id].data[0, :, 1:4] (x,y,z)
 - Stress: op2.cquad4_stress[subcase_id] or op2.ctria3_stress[subcase_id]
 - Eigenvalues: op2.eigenvalues[subcase_id]
+- Mass: op2.grid_point_weight (if available)
 
-Return ONLY the complete Python code wrapped in ```python ... ```. No explanations outside the code block."""
+Return ONLY the complete Python code wrapped in ```python ... ```. No explanations."""
 
     # Build user prompt with context
     user_prompt = f"Generate a custom extractor that: {request.prompt}"

atomizer-dashboard/backend/api/routes/devloop.py

@@ -0,0 +1,416 @@
+"""
+DevLoop API Endpoints - Closed-loop development orchestration.
+
+Provides REST API and WebSocket for:
+- Starting/stopping development cycles
+- Monitoring progress
+- Executing single phases
+- Viewing history and learnings
+"""
+
+from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect, BackgroundTasks
+from pydantic import BaseModel, Field
+from typing import Any, Dict, List, Optional
+import asyncio
+import json
+import sys
+from pathlib import Path
+from datetime import datetime
+
+# Add project root to path
+sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
+
+router = APIRouter(prefix="/devloop", tags=["devloop"])
+
+# Global orchestrator instance
+_orchestrator = None
+_active_cycle = None
+_websocket_clients: List[WebSocket] = []
+
+
+def get_orchestrator():
+    """Get or create the DevLoop orchestrator."""
+    global _orchestrator
+    if _orchestrator is None:
+        from optimization_engine.devloop import DevLoopOrchestrator
+
+        _orchestrator = DevLoopOrchestrator(
+            {
+                "dashboard_url": "http://localhost:8000",
+                "websocket_url": "ws://localhost:8000",
+                "studies_dir": str(Path(__file__).parent.parent.parent.parent.parent / "studies"),
+                "learning_enabled": True,
+            }
+        )
+
+        # Subscribe to state updates
+        _orchestrator.subscribe(_broadcast_state_update)
+
+    return _orchestrator
+
+
+def _broadcast_state_update(state):
+    """Broadcast state updates to all WebSocket clients."""
+    asyncio.create_task(
+        _send_to_all_clients(
+            {
+                "type": "state_update",
+                "state": {
+                    "phase": state.phase.value,
+                    "iteration": state.iteration,
+                    "current_task": state.current_task,
+                    "last_update": state.last_update,
+                },
+            }
+        )
+    )
+
+
+async def _send_to_all_clients(message: Dict):
+    """Send message to all connected WebSocket clients."""
+    disconnected = []
+    for client in _websocket_clients:
+        try:
+            await client.send_json(message)
+        except Exception:
+            disconnected.append(client)
+
+    # Clean up disconnected clients
+    for client in disconnected:
+        if client in _websocket_clients:
+            _websocket_clients.remove(client)
+
+
+# ============================================================================
+# Request/Response Models
+# ============================================================================
+
+
+class StartCycleRequest(BaseModel):
+    """Request to start a development cycle."""
+
+    objective: str = Field(..., description="What to achieve")
+    context: Optional[Dict[str, Any]] = Field(default=None, description="Additional context")
+    max_iterations: Optional[int] = Field(default=10, description="Maximum iterations")
+
+
+class StepRequest(BaseModel):
+    """Request to execute a single step."""
+
+    phase: str = Field(..., description="Phase to execute: plan, implement, test, analyze")
+    data: Optional[Dict[str, Any]] = Field(default=None, description="Phase-specific data")
+
+
+class CycleStatusResponse(BaseModel):
+    """Response with cycle status."""
+
+    active: bool
+    phase: str
+    iteration: int
+    current_task: Optional[str]
+    last_update: str
+
+
+# ============================================================================
+# REST Endpoints
+# ============================================================================
+
+
+@router.get("/status")
+async def get_status() -> CycleStatusResponse:
+    """Get current DevLoop status."""
+    orchestrator = get_orchestrator()
+    state = orchestrator.get_state()
+
+    return CycleStatusResponse(
+        active=state["phase"] != "idle",
+        phase=state["phase"],
+        iteration=state["iteration"],
+        current_task=state.get("current_task"),
+        last_update=state["last_update"],
+    )
+
+
+@router.post("/start")
+async def start_cycle(request: StartCycleRequest, background_tasks: BackgroundTasks):
+    """
+    Start a new development cycle.
+
+    The cycle runs in the background and broadcasts progress via WebSocket.
+    """
+    global _active_cycle
+
+    orchestrator = get_orchestrator()
+
+    # Check if already running
+    if orchestrator.state.phase.value != "idle":
+        raise HTTPException(status_code=409, detail="A development cycle is already running")
+
+    # Start cycle in background
+    async def run_cycle():
+        global _active_cycle
+        try:
+            result = await orchestrator.run_development_cycle(
+                objective=request.objective,
+                context=request.context,
+                max_iterations=request.max_iterations,
+            )
+            _active_cycle = result
+
+            # Broadcast completion
+            await _send_to_all_clients(
+                {
+                    "type": "cycle_complete",
+                    "result": {
+                        "objective": result.objective,
+                        "status": result.status,
+                        "iterations": len(result.iterations),
+                        "duration_seconds": result.total_duration_seconds,
+                    },
+                }
+            )
+        except Exception as e:
+            await _send_to_all_clients({"type": "cycle_error", "error": str(e)})
+
+    background_tasks.add_task(run_cycle)
+
+    return {
+        "message": "Development cycle started",
+        "objective": request.objective,
+    }
+
+
+@router.post("/stop")
+async def stop_cycle():
+    """Stop the current development cycle."""
+    orchestrator = get_orchestrator()
+
+    if orchestrator.state.phase.value == "idle":
+        raise HTTPException(status_code=400, detail="No active cycle to stop")
+
+    # Set state to idle (will stop at next phase boundary)
+    orchestrator._update_state(phase=orchestrator.state.phase.__class__.IDLE, task="Stopping...")
+
+    return {"message": "Cycle stop requested"}
+
+
+@router.post("/step")
+async def execute_step(request: StepRequest):
+    """
+    Execute a single phase step.
+
+    Useful for manual control or debugging.
+    """
+    orchestrator = get_orchestrator()
+
+    if request.phase == "plan":
+        objective = request.data.get("objective", "") if request.data else ""
+        context = request.data.get("context") if request.data else None
+        result = await orchestrator.step_plan(objective, context)
+
+    elif request.phase == "implement":
+        plan = request.data if request.data else {}
+        result = await orchestrator.step_implement(plan)
+
+    elif request.phase == "test":
+        scenarios = request.data.get("scenarios", []) if request.data else []
+        result = await orchestrator.step_test(scenarios)
+
+    elif request.phase == "analyze":
+        test_results = request.data if request.data else {}
+        result = await orchestrator.step_analyze(test_results)
+
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unknown phase: {request.phase}. Valid: plan, implement, test, analyze",
+        )
+
+    return {"phase": request.phase, "result": result}
+
+
+@router.get("/history")
+async def get_history():
+    """Get history of past development cycles."""
+    orchestrator = get_orchestrator()
+    return orchestrator.export_history()
+
+
+@router.get("/last-cycle")
+async def get_last_cycle():
+    """Get details of the most recent cycle."""
+    global _active_cycle
+
+    if _active_cycle is None:
+        raise HTTPException(status_code=404, detail="No cycle has been run yet")
+
+    return {
+        "objective": _active_cycle.objective,
+        "status": _active_cycle.status,
+        "start_time": _active_cycle.start_time,
+        "end_time": _active_cycle.end_time,
+        "iterations": [
+            {
+                "iteration": it.iteration,
+                "success": it.success,
+                "duration_seconds": it.duration_seconds,
+                "has_plan": it.plan is not None,
+                "has_tests": it.test_results is not None,
+                "has_fixes": it.fixes is not None,
+            }
+            for it in _active_cycle.iterations
+        ],
+        "total_duration_seconds": _active_cycle.total_duration_seconds,
+    }
+
+
+@router.get("/health")
+async def health_check():
+    """Check DevLoop system health."""
+    orchestrator = get_orchestrator()
+
+    # Check dashboard connection
+    from optimization_engine.devloop import DashboardTestRunner
+
+    runner = DashboardTestRunner()
+    dashboard_health = await runner.run_health_check()
+
+    return {
+        "devloop": "healthy",
+        "orchestrator_state": orchestrator.get_state()["phase"],
+        "dashboard": dashboard_health,
+    }
+
+
+# ============================================================================
+# WebSocket Endpoint
+# ============================================================================
+
+
+@router.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    """
+    WebSocket endpoint for real-time DevLoop updates.
+
+    Messages sent:
+    - state_update: Phase/iteration changes
+    - cycle_complete: Cycle finished
+    - cycle_error: Cycle failed
+    - test_progress: Individual test results
+    """
+    await websocket.accept()
+    _websocket_clients.append(websocket)
+
+    orchestrator = get_orchestrator()
+
+    try:
+        # Send initial state
+        await websocket.send_json(
+            {
+                "type": "connection_ack",
+                "state": orchestrator.get_state(),
+            }
+        )
+
+        # Handle incoming messages
+        while True:
+            try:
+                data = await asyncio.wait_for(websocket.receive_json(), timeout=30.0)
+
+                msg_type = data.get("type")
+
+                if msg_type == "ping":
+                    await websocket.send_json({"type": "pong"})
+
+                elif msg_type == "get_state":
+                    await websocket.send_json(
+                        {
+                            "type": "state",
+                            "state": orchestrator.get_state(),
+                        }
+                    )
+
+                elif msg_type == "start_cycle":
+                    # Allow starting cycle via WebSocket
+                    objective = data.get("objective", "")
+                    context = data.get("context")
+
+                    asyncio.create_task(orchestrator.run_development_cycle(objective, context))
+
+                    await websocket.send_json(
+                        {
+                            "type": "cycle_started",
+                            "objective": objective,
+                        }
+                    )
+
+            except asyncio.TimeoutError:
+                # Send heartbeat
+                await websocket.send_json({"type": "heartbeat"})
+
+    except WebSocketDisconnect:
+        pass
+    finally:
+        if websocket in _websocket_clients:
+            _websocket_clients.remove(websocket)
+
+
+# ============================================================================
+# Convenience Endpoints for Common Tasks
+# ============================================================================
+
+
+@router.post("/create-study")
+async def create_study_cycle(
+    study_name: str,
+    problem_statement: Optional[str] = None,
+    background_tasks: BackgroundTasks = None,
+):
+    """
+    Convenience endpoint to start a study creation cycle.
+
+    This is a common workflow that combines planning, implementation, and testing.
+    """
+    orchestrator = get_orchestrator()
+
+    context = {
+        "study_name": study_name,
+        "task_type": "create_study",
+    }
+
+    if problem_statement:
+        context["problem_statement"] = problem_statement
+
+    # Start the cycle
+    async def run_cycle():
+        result = await orchestrator.run_development_cycle(
+            objective=f"Create optimization study: {study_name}",
+            context=context,
+        )
+        return result
+
+    if background_tasks:
+        background_tasks.add_task(run_cycle)
+        return {"message": f"Study creation cycle started for '{study_name}'"}
+    else:
+        result = await run_cycle()
+        return {
+            "message": f"Study '{study_name}' creation completed",
+            "status": result.status,
+            "iterations": len(result.iterations),
+        }
+
+
+@router.post("/run-tests")
+async def run_tests(scenarios: List[Dict[str, Any]]):
+    """
+    Run a set of test scenarios directly.
+
+    Useful for testing specific features without a full cycle.
+    """
+    from optimization_engine.devloop import DashboardTestRunner
+
+    runner = DashboardTestRunner()
+    results = await runner.run_test_suite(scenarios)
+
+    return results

atomizer-dashboard/backend/api/services/claude_readme.py

@@ -0,0 +1,396 @@
+"""
+Claude README Generator Service
+
+Generates intelligent README.md files for optimization studies
+using Claude Code CLI (not API) with study context from AtomizerSpec.
+"""
+
+import asyncio
+import json
+import subprocess
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+# Base directory
+ATOMIZER_ROOT = Path(__file__).parent.parent.parent.parent.parent
+
+# Load skill prompt
+SKILL_PATH = ATOMIZER_ROOT / ".claude" / "skills" / "modules" / "study-readme-generator.md"
+
+
+def load_skill_prompt() -> str:
+    """Load the README generator skill prompt."""
+    if SKILL_PATH.exists():
+        return SKILL_PATH.read_text(encoding="utf-8")
+    return ""
+
+
+class ClaudeReadmeGenerator:
+    """Generate README.md files using Claude Code CLI."""
+
+    def __init__(self):
+        self.skill_prompt = load_skill_prompt()
+
+    def generate_readme(
+        self,
+        study_name: str,
+        spec: Dict[str, Any],
+        context_files: Optional[Dict[str, str]] = None,
+        topic: Optional[str] = None,
+    ) -> str:
+        """
+        Generate a README.md for a study using Claude Code CLI.
+
+        Args:
+            study_name: Name of the study
+            spec: Full AtomizerSpec v2.0 dict
+            context_files: Optional dict of {filename: content} for context
+            topic: Optional topic folder name
+
+        Returns:
+            Generated README.md content
+        """
+        # Build context for Claude
+        context = self._build_context(study_name, spec, context_files, topic)
+
+        # Build the prompt
+        prompt = self._build_prompt(context)
+
+        try:
+            # Run Claude Code CLI synchronously
+            result = self._run_claude_cli(prompt)
+
+            # Extract markdown content from response
+            readme_content = self._extract_markdown(result)
+
+            if readme_content:
+                return readme_content
+
+            # If no markdown found, return the whole response
+            return result if result else self._generate_fallback_readme(study_name, spec)
+
+        except Exception as e:
+            print(f"Claude CLI error: {e}")
+            return self._generate_fallback_readme(study_name, spec)
+
+    async def generate_readme_async(
+        self,
+        study_name: str,
+        spec: Dict[str, Any],
+        context_files: Optional[Dict[str, str]] = None,
+        topic: Optional[str] = None,
+    ) -> str:
+        """Async version of generate_readme."""
+        # Run in thread pool to not block
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None, lambda: self.generate_readme(study_name, spec, context_files, topic)
+        )
+
+    def _run_claude_cli(self, prompt: str) -> str:
+        """Run Claude Code CLI and get response."""
+        try:
+            # Use claude CLI with --print flag for non-interactive output
+            result = subprocess.run(
+                ["claude", "--print", prompt],
+                capture_output=True,
+                text=True,
+                timeout=120,  # 2 minute timeout
+                cwd=str(ATOMIZER_ROOT),
+            )
+
+            if result.returncode != 0:
+                error_msg = result.stderr or "Unknown error"
+                raise Exception(f"Claude CLI error: {error_msg}")
+
+            return result.stdout.strip()
+
+        except subprocess.TimeoutExpired:
+            raise Exception("Request timed out")
+        except FileNotFoundError:
+            raise Exception("Claude CLI not found. Make sure 'claude' is in PATH.")
+
+    def _build_context(
+        self,
+        study_name: str,
+        spec: Dict[str, Any],
+        context_files: Optional[Dict[str, str]],
+        topic: Optional[str],
+    ) -> Dict[str, Any]:
+        """Build the context object for Claude."""
+        meta = spec.get("meta", {})
+        model = spec.get("model", {})
+        introspection = model.get("introspection", {}) or {}
+
+        context = {
+            "study_name": study_name,
+            "topic": topic or meta.get("topic", "Other"),
+            "description": meta.get("description", ""),
+            "created": meta.get("created", datetime.now().isoformat()),
+            "status": meta.get("status", "draft"),
+            "design_variables": spec.get("design_variables", []),
+            "extractors": spec.get("extractors", []),
+            "objectives": spec.get("objectives", []),
+            "constraints": spec.get("constraints", []),
+            "optimization": spec.get("optimization", {}),
+            "introspection": {
+                "mass_kg": introspection.get("mass_kg"),
+                "volume_mm3": introspection.get("volume_mm3"),
+                "solver_type": introspection.get("solver_type"),
+                "expressions": introspection.get("expressions", []),
+                "expressions_count": len(introspection.get("expressions", [])),
+            },
+            "model_files": {
+                "sim": model.get("sim", {}).get("path") if model.get("sim") else None,
+                "prt": model.get("prt", {}).get("path") if model.get("prt") else None,
+                "fem": model.get("fem", {}).get("path") if model.get("fem") else None,
+            },
+        }
+
+        # Add context files if provided
+        if context_files:
+            context["context_files"] = context_files
+
+        return context
+
+    def _build_prompt(self, context: Dict[str, Any]) -> str:
+        """Build the prompt for Claude CLI."""
+
+        # Build context files section if available
+        context_files_section = ""
+        if context.get("context_files"):
+            context_files_section = "\n\n## User-Provided Context Files\n\nIMPORTANT: Use this information to understand the optimization goals, design variables, objectives, and constraints:\n\n"
+            for filename, content in context.get("context_files", {}).items():
+                context_files_section += f"### {filename}\n```\n{content}\n```\n\n"
+
+        # Remove context_files from JSON dump to avoid duplication
+        context_for_json = {k: v for k, v in context.items() if k != "context_files"}
+
+        prompt = f"""Generate a README.md for this FEA optimization study.
+
+## Study Technical Data
+
+```json
+{json.dumps(context_for_json, indent=2, default=str)}
+```
+{context_files_section}
+## Requirements
+
+1. Use the EXACT values from the technical data - no placeholders
+2. If context files are provided, extract:
+   - Design variable bounds (min/max)
+   - Optimization objectives (minimize/maximize what)
+   - Constraints (stress limits, etc.)
+   - Any specific requirements mentioned
+
+3. Format the README with these sections:
+   - Title (# Study Name)
+   - Overview (topic, date, status, description from context)
+   - Engineering Problem (what we're optimizing and why - from context files)
+   - Model Information (mass, solver, files)
+   - Design Variables (if context specifies bounds, include them in a table)
+   - Optimization Objectives (from context files)
+   - Constraints (from context files)
+   - Expressions Found (table of discovered expressions, highlight candidates)
+   - Next Steps (what needs to be configured)
+
+4. Keep it professional and concise
+5. Use proper markdown table formatting
+6. Include units where applicable
+7. For expressions table, show: name, value, units, is_candidate
+
+Generate ONLY the README.md content in markdown format, no explanations:"""
+
+        return prompt
+
+    def _extract_markdown(self, response: str) -> Optional[str]:
+        """Extract markdown content from Claude response."""
+        if not response:
+            return None
+
+        # If response starts with #, it's already markdown
+        if response.strip().startswith("#"):
+            return response.strip()
+
+        # Try to find markdown block
+        if "```markdown" in response:
+            start = response.find("```markdown") + len("```markdown")
+            end = response.find("```", start)
+            if end > start:
+                return response[start:end].strip()
+
+        if "```md" in response:
+            start = response.find("```md") + len("```md")
+            end = response.find("```", start)
+            if end > start:
+                return response[start:end].strip()
+
+        # Look for first # heading
+        lines = response.split("\n")
+        for i, line in enumerate(lines):
+            if line.strip().startswith("# "):
+                return "\n".join(lines[i:]).strip()
+
+        return None
+
+    def _generate_fallback_readme(self, study_name: str, spec: Dict[str, Any]) -> str:
+        """Generate a basic README if Claude fails."""
+        meta = spec.get("meta", {})
+        model = spec.get("model", {})
+        introspection = model.get("introspection", {}) or {}
+        dvs = spec.get("design_variables", [])
+        objs = spec.get("objectives", [])
+        cons = spec.get("constraints", [])
+        opt = spec.get("optimization", {})
+        expressions = introspection.get("expressions", [])
+
+        lines = [
+            f"# {study_name.replace('_', ' ').title()}",
+            "",
+            f"**Topic**: {meta.get('topic', 'Other')}",
+            f"**Created**: {meta.get('created', 'Unknown')[:10] if meta.get('created') else 'Unknown'}",
+            f"**Status**: {meta.get('status', 'draft')}",
+            "",
+        ]
+
+        if meta.get("description"):
+            lines.extend([meta["description"], ""])
+
+        # Model Information
+        lines.extend(
+            [
+                "## Model Information",
+                "",
+            ]
+        )
+
+        if introspection.get("mass_kg"):
+            lines.append(f"- **Mass**: {introspection['mass_kg']:.2f} kg")
+
+        sim_path = model.get("sim", {}).get("path") if model.get("sim") else None
+        if sim_path:
+            lines.append(f"- **Simulation**: {sim_path}")
+
+        lines.append("")
+
+        # Expressions Found
+        if expressions:
+            lines.extend(
+                [
+                    "## Expressions Found",
+                    "",
+                    "| Name | Value | Units | Candidate |",
+                    "|------|-------|-------|-----------|",
+                ]
+            )
+            for expr in expressions:
+                is_candidate = "✓" if expr.get("is_candidate") else ""
+                value = f"{expr.get('value', '-')}"
+                units = expr.get("units", "-")
+                lines.append(f"| {expr.get('name', '-')} | {value} | {units} | {is_candidate} |")
+            lines.append("")
+
+        # Design Variables (if configured)
+        if dvs:
+            lines.extend(
+                [
+                    "## Design Variables",
+                    "",
+                    "| Variable | Expression | Range | Units |",
+                    "|----------|------------|-------|-------|",
+                ]
+            )
+            for dv in dvs:
+                bounds = dv.get("bounds", {})
+                units = dv.get("units", "-")
+                lines.append(
+                    f"| {dv.get('name', 'Unknown')} | "
+                    f"{dv.get('expression_name', '-')} | "
+                    f"[{bounds.get('min', '-')}, {bounds.get('max', '-')}] | "
+                    f"{units} |"
+                )
+            lines.append("")
+
+        # Objectives
+        if objs:
+            lines.extend(
+                [
+                    "## Objectives",
+                    "",
+                    "| Objective | Direction | Weight |",
+                    "|-----------|-----------|--------|",
+                ]
+            )
+            for obj in objs:
+                lines.append(
+                    f"| {obj.get('name', 'Unknown')} | "
+                    f"{obj.get('direction', 'minimize')} | "
+                    f"{obj.get('weight', 1.0)} |"
+                )
+            lines.append("")
+
+        # Constraints
+        if cons:
+            lines.extend(
+                [
+                    "## Constraints",
+                    "",
+                    "| Constraint | Condition | Threshold |",
+                    "|------------|-----------|-----------|",
+                ]
+            )
+            for con in cons:
+                lines.append(
+                    f"| {con.get('name', 'Unknown')} | "
+                    f"{con.get('operator', '<=')} | "
+                    f"{con.get('threshold', '-')} |"
+                )
+            lines.append("")
+
+        # Algorithm
+        algo = opt.get("algorithm", {})
+        budget = opt.get("budget", {})
+        lines.extend(
+            [
+                "## Methodology",
+                "",
+                f"- **Algorithm**: {algo.get('type', 'TPE')}",
+                f"- **Max Trials**: {budget.get('max_trials', 100)}",
+                "",
+            ]
+        )
+
+        # Next Steps
+        lines.extend(
+            [
+                "## Next Steps",
+                "",
+            ]
+        )
+
+        if not dvs:
+            lines.append("- [ ] Configure design variables from discovered expressions")
+        if not objs:
+            lines.append("- [ ] Define optimization objectives")
+        if not dvs and not objs:
+            lines.append("- [ ] Open in Canvas Builder to complete configuration")
+        else:
+            lines.append("- [ ] Run baseline solve to validate setup")
+            lines.append("- [ ] Finalize study to move to studies folder")
+
+        lines.append("")
+
+        return "\n".join(lines)
+
+
+# Singleton instance
+_generator: Optional[ClaudeReadmeGenerator] = None
+
+
+def get_readme_generator() -> ClaudeReadmeGenerator:
+    """Get the singleton README generator instance."""
+    global _generator
+    if _generator is None:
+        _generator = ClaudeReadmeGenerator()
+    return _generator

atomizer-dashboard/backend/api/services/context_builder.py

@@ -26,6 +26,7 @@ class ContextBuilder:
         study_id: Optional[str] = None,
         conversation_history: Optional[List[Dict[str, Any]]] = None,
         canvas_state: Optional[Dict[str, Any]] = None,
+        spec_path: Optional[str] = None,
     ) -> str:
         """
         Build full system prompt with context.
@@ -35,6 +36,7 @@ class ContextBuilder:
             study_id: Optional study name to provide context for
             conversation_history: Optional recent messages for continuity
             canvas_state: Optional canvas state (nodes, edges) from the UI
+            spec_path: Optional path to the atomizer_spec.json file
 
         Returns:
             Complete system prompt string
@@ -45,7 +47,7 @@ class ContextBuilder:
         if canvas_state:
             node_count = len(canvas_state.get("nodes", []))
             print(f"[ContextBuilder] Including canvas context with {node_count} nodes")
-            parts.append(self._canvas_context(canvas_state))
+            parts.append(self._canvas_context(canvas_state, spec_path))
         else:
             print("[ContextBuilder] No canvas state provided")
 
@@ -57,7 +59,7 @@ class ContextBuilder:
         if conversation_history:
             parts.append(self._conversation_context(conversation_history))
 
-        parts.append(self._mode_instructions(mode))
+        parts.append(self._mode_instructions(mode, spec_path))
 
         return "\n\n---\n\n".join(parts)
 
@@ -298,7 +300,7 @@ Important guidelines:
 
         return context
 
-    def _canvas_context(self, canvas_state: Dict[str, Any]) -> str:
+    def _canvas_context(self, canvas_state: Dict[str, Any], spec_path: Optional[str] = None) -> str:
         """
         Build context from canvas state (nodes and edges).
 
@@ -317,6 +319,8 @@ Important guidelines:
         context += f"**Study Name**: {study_name}\n"
         if study_path:
             context += f"**Study Path**: {study_path}\n"
+        if spec_path:
+            context += f"**Spec File**: `{spec_path}`\n"
         context += "\n"
 
         # Group nodes by type
@@ -438,61 +442,100 @@ Important guidelines:
         context += f"Total edges: {len(edges)}\n"
         context += "Flow: Design Variables → Model → Solver → Extractors → Objectives/Constraints → Algorithm\n\n"
 
-        # Canvas modification instructions
-        context += """## Canvas Modification Tools
-
-**For AtomizerSpec v2.0 studies (preferred):**
-Use spec tools when working with v2.0 studies (check if study uses `atomizer_spec.json`):
-- `spec_modify` - Modify spec values using JSONPath (e.g., "design_variables[0].bounds.min")
-- `spec_add_node` - Add design variables, extractors, objectives, or constraints
-- `spec_remove_node` - Remove nodes from the spec
-- `spec_add_custom_extractor` - Add a Python-based custom extractor function
-
-**For Legacy Canvas (optimization_config.json):**
-- `canvas_add_node` - Add a new node (designVar, extractor, objective, constraint)
-- `canvas_update_node` - Update node properties (bounds, weights, names)
-- `canvas_remove_node` - Remove a node from the canvas
-- `canvas_connect_nodes` - Create an edge between nodes
-
-**Example user requests you can handle:**
-- "Add a design variable called hole_diameter with range 5-15 mm" → Use spec_add_node or canvas_add_node
-- "Change the weight of wfe_40_20 to 8" → Use spec_modify or canvas_update_node
-- "Remove the constraint node" → Use spec_remove_node or canvas_remove_node
-- "Add a custom extractor that computes stress ratio" → Use spec_add_custom_extractor
-
-Always respond with confirmation of changes made to the canvas/spec.
-"""
-
+        # Instructions will be in _mode_instructions based on spec_path
         return context
 
-    def _mode_instructions(self, mode: str) -> str:
+    def _mode_instructions(self, mode: str, spec_path: Optional[str] = None) -> str:
         """Mode-specific instructions"""
         if mode == "power":
-            return """# Power Mode Instructions
+            instructions = """# Power Mode Instructions
 
 You have **FULL ACCESS** to modify Atomizer studies. **DO NOT ASK FOR PERMISSION** - just do it.
 
-## Direct Actions (no confirmation needed):
-- **Add design variables**: Use `canvas_add_node` or `spec_add_node` with node_type="designVar"
-- **Add extractors**: Use `canvas_add_node` with node_type="extractor"
-- **Add objectives**: Use `canvas_add_node` with node_type="objective"
-- **Add constraints**: Use `canvas_add_node` with node_type="constraint"
-- **Update node properties**: Use `canvas_update_node` or `spec_modify`
-- **Remove nodes**: Use `canvas_remove_node`
-- **Edit atomizer_spec.json directly**: Use the Edit tool
+## CRITICAL: How to Modify the Spec
 
-## For custom extractors with Python code:
-Use `spec_add_custom_extractor` to add a custom function.
-
-## IMPORTANT:
-- You have --dangerously-skip-permissions enabled
-- The user has explicitly granted you power mode access
-- **ACT IMMEDIATELY** when asked to add/modify/remove things
-- Explain what you did AFTER doing it, not before
-- Do NOT say "I need permission" - you already have it
-
-Example: If user says "add a volume extractor", immediately use canvas_add_node to add it.
 """
+            if spec_path:
+                instructions += f"""**The spec file is at**: `{spec_path}`
+
+When asked to add/modify/remove design variables, extractors, objectives, or constraints:
+1. **Read the spec file first** using the Read tool
+2. **Edit the spec file** using the Edit tool to make precise changes
+3. **Confirm what you changed** in your response
+
+### AtomizerSpec v2.0 Structure
+
+The spec has these main arrays you can modify:
+- `design_variables` - Parameters to optimize
+- `extractors` - Physics extraction functions  
+- `objectives` - What to minimize/maximize
+- `constraints` - Limits that must be satisfied
+
+### Example: Add a Design Variable
+
+To add a design variable called "thickness" with bounds [1, 10]:
+
+1. Read the spec: `Read({spec_path})`
+2. Find the `"design_variables": [...]` array
+3. Add a new entry like:
+```json
+{{
+  "id": "dv_thickness",
+  "name": "thickness",
+  "expression_name": "thickness",
+  "type": "continuous",
+  "bounds": {{"min": 1, "max": 10}},
+  "baseline": 5,
+  "units": "mm",
+  "enabled": true
+}}
+```
+4. Use Edit tool to insert this into the array
+
+### Example: Add an Objective
+
+To add a "minimize mass" objective:
+```json
+{{
+  "id": "obj_mass",
+  "name": "mass",
+  "direction": "minimize",
+  "weight": 1.0,
+  "source": {{
+    "extractor_id": "ext_mass",
+    "output_name": "mass"
+  }}
+}}
+```
+
+### Example: Add an Extractor
+
+To add a mass extractor:
+```json
+{{
+  "id": "ext_mass",
+  "name": "mass",
+  "type": "mass",
+  "builtin": true,
+  "outputs": [{{"name": "mass", "units": "kg"}}]
+}}
+```
+
+"""
+            else:
+                instructions += """No spec file is currently set. Ask the user which study they want to work with.
+
+"""
+
+            instructions += """## IMPORTANT Rules:
+- You have --dangerously-skip-permissions enabled
+- **ACT IMMEDIATELY** when asked to add/modify/remove things
+- Use the **Edit** tool to modify the spec file directly
+- Generate unique IDs like `dv_<name>`, `ext_<name>`, `obj_<name>`, `con_<name>`
+- Explain what you changed AFTER doing it, not before
+- Do NOT say "I need permission" - you already have it
+"""
+            return instructions
         else:
             return """# User Mode Instructions
 
@@ -503,29 +546,11 @@ You can help with optimization workflows:
 - Generate reports
 - Explain FEA concepts
 
-**For code modifications**, suggest switching to Power Mode.
+**For modifying studies**, the user needs to switch to Power Mode.
 
-Available tools:
-- `list_studies`, `get_study_status`, `create_study`
-- `run_optimization`, `stop_optimization`, `get_optimization_status`
-- `get_trial_data`, `analyze_convergence`, `compare_trials`, `get_best_design`
-- `generate_report`, `export_data`
-- `explain_physics`, `recommend_method`, `query_extractors`
-
-**AtomizerSpec v2.0 Tools (preferred for new studies):**
-- `spec_get` - Get the full AtomizerSpec for a study
-- `spec_modify` - Modify spec values using JSONPath (e.g., "design_variables[0].bounds.min")
-- `spec_add_node` - Add design variables, extractors, objectives, or constraints
-- `spec_remove_node` - Remove nodes from the spec
-- `spec_validate` - Validate spec against JSON Schema
-- `spec_add_custom_extractor` - Add a Python-based custom extractor function
-- `spec_create_from_description` - Create a new study from natural language description
-
-**Canvas Tools (for visual workflow builder):**
-- `validate_canvas_intent` - Validate a canvas-generated optimization intent
-- `execute_canvas_intent` - Create a study from a canvas intent
-- `interpret_canvas_intent` - Analyze intent and provide recommendations
-
-When you receive a message containing "INTENT:" followed by JSON, this is from the Canvas UI.
-Parse the intent and use the appropriate canvas tool to process it.
+In user mode you can:
+- Read and explain study configurations
+- Analyze optimization results
+- Provide recommendations
+- Answer questions about FEA and optimization
 """

atomizer-dashboard/backend/api/services/session_manager.py

@@ -1,11 +1,15 @@
 """
 Session Manager
 
-Manages persistent Claude Code sessions with MCP integration.
+Manages persistent Claude Code sessions with direct file editing.
 Fixed for Windows compatibility - uses subprocess.Popen with ThreadPoolExecutor.
+
+Strategy: Claude edits atomizer_spec.json directly using Edit/Write tools
+(no MCP dependency for reliability).
 """
 
 import asyncio
+import hashlib
 import json
 import os
 import subprocess
@@ -26,6 +30,10 @@ MCP_SERVER_PATH = ATOMIZER_ROOT / "mcp-server" / "atomizer-tools"
 # Thread pool for subprocess operations (Windows compatible)
 _executor = ThreadPoolExecutor(max_workers=4)
 
+import logging
+
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class ClaudeSession:
@@ -130,6 +138,7 @@ class SessionManager:
         Send a message to a session and stream the response.
 
         Uses synchronous subprocess.Popen via ThreadPoolExecutor for Windows compatibility.
+        Claude edits atomizer_spec.json directly using Edit/Write tools (no MCP).
 
         Args:
             session_id: The session ID
@@ -147,45 +156,48 @@ class SessionManager:
         # Store user message
         self.store.add_message(session_id, "user", message)
 
+        # Get spec path and hash BEFORE Claude runs (to detect changes)
+        spec_path = self._get_spec_path(session.study_id) if session.study_id else None
+        spec_hash_before = self._get_file_hash(spec_path) if spec_path else None
+
         # Build context with conversation history AND canvas state
         history = self.store.get_history(session_id, limit=10)
         full_prompt = self.context_builder.build(
             mode=session.mode,
             study_id=session.study_id,
             conversation_history=history[:-1],
-            canvas_state=canvas_state,  # Pass canvas state for context
+            canvas_state=canvas_state,
+            spec_path=str(spec_path) if spec_path else None,  # Tell Claude where the spec is
         )
         full_prompt += f"\n\nUser: {message}\n\nRespond helpfully and concisely:"
 
-        # Build CLI arguments
+        # Build CLI arguments - NO MCP for reliability
         cli_args = ["claude", "--print"]
 
-        # Ensure MCP config exists
-        mcp_config_path = ATOMIZER_ROOT / f".claude-mcp-{session_id}.json"
-        if not mcp_config_path.exists():
-            mcp_config = self._build_mcp_config(session.mode)
-            with open(mcp_config_path, "w") as f:
-                json.dump(mcp_config, f)
-        cli_args.extend(["--mcp-config", str(mcp_config_path)])
-
         if session.mode == "user":
-            cli_args.extend([
-                "--allowedTools",
-                "Read Write(**/STUDY_REPORT.md) Write(**/3_results/*.md) Bash(python:*) mcp__atomizer-tools__*"
-            ])
+            # User mode: limited tools
+            cli_args.extend(
+                [
+                    "--allowedTools",
+                    "Read Bash(python:*)",
+                ]
+            )
         else:
+            # Power mode: full access to edit files
             cli_args.append("--dangerously-skip-permissions")
 
         cli_args.append("-")  # Read from stdin
 
         full_response = ""
         tool_calls: List[Dict] = []
+        process: Optional[subprocess.Popen] = None
 
         try:
             loop = asyncio.get_event_loop()
 
             # Run subprocess in thread pool (Windows compatible)
             def run_claude():
+                nonlocal process
                 try:
                     process = subprocess.Popen(
                         cli_args,
@@ -194,8 +206,8 @@ class SessionManager:
                         stderr=subprocess.PIPE,
                         cwd=str(ATOMIZER_ROOT),
                         text=True,
-                        encoding='utf-8',
-                        errors='replace',
+                        encoding="utf-8",
+                        errors="replace",
                     )
                     stdout, stderr = process.communicate(input=full_prompt, timeout=300)
                     return {
@@ -204,10 +216,13 @@ class SessionManager:
                         "returncode": process.returncode,
                     }
                 except subprocess.TimeoutExpired:
-                    process.kill()
+                    if process:
+                        process.kill()
                     return {"error": "Response timeout (5 minutes)"}
                 except FileNotFoundError:
-                    return {"error": "Claude CLI not found in PATH. Install with: npm install -g @anthropic-ai/claude-code"}
+                    return {
+                        "error": "Claude CLI not found in PATH. Install with: npm install -g @anthropic-ai/claude-code"
+                    }
                 except Exception as e:
                     return {"error": str(e)}
 
@@ -219,24 +234,14 @@ class SessionManager:
                 full_response = result["stdout"] or ""
 
                 if full_response:
-                    # Check if response contains canvas modifications (from MCP tools)
-                    import logging
-                    logger = logging.getLogger(__name__)
-
-                    modifications = self._extract_canvas_modifications(full_response)
-                    logger.info(f"[SEND_MSG] Found {len(modifications)} canvas modifications to send")
-
-                    for mod in modifications:
-                        logger.info(f"[SEND_MSG] Sending canvas_modification: {mod.get('action')} {mod.get('nodeType')}")
-                        yield {"type": "canvas_modification", "modification": mod}
-
-                    # Always send the text response
+                    # Always send the text response first
                     yield {"type": "text", "content": full_response}
 
                 if result["returncode"] != 0 and result["stderr"]:
-                    yield {"type": "error", "message": f"CLI error: {result['stderr']}"}
+                    logger.warning(f"[SEND_MSG] CLI stderr: {result['stderr']}")
 
         except Exception as e:
+            logger.error(f"[SEND_MSG] Exception: {e}")
             yield {"type": "error", "message": str(e)}
 
         # Store assistant response
@@ -248,8 +253,46 @@ class SessionManager:
                 tool_calls=tool_calls if tool_calls else None,
             )
 
+        # Check if spec was modified by comparing hashes
+        if spec_path and session.mode == "power" and session.study_id:
+            spec_hash_after = self._get_file_hash(spec_path)
+            if spec_hash_before != spec_hash_after:
+                logger.info(f"[SEND_MSG] Spec file was modified! Sending update.")
+                spec_update = await self._check_spec_updated(session.study_id)
+                if spec_update:
+                    yield {
+                        "type": "spec_updated",
+                        "spec": spec_update,
+                        "tool": "direct_edit",
+                        "reason": "Claude modified spec file directly",
+                    }
+
         yield {"type": "done", "tool_calls": tool_calls}
 
+    def _get_spec_path(self, study_id: str) -> Optional[Path]:
+        """Get the atomizer_spec.json path for a study."""
+        if not study_id:
+            return None
+
+        if study_id.startswith("draft_"):
+            spec_path = ATOMIZER_ROOT / "studies" / "_inbox" / study_id / "atomizer_spec.json"
+        else:
+            spec_path = ATOMIZER_ROOT / "studies" / study_id / "atomizer_spec.json"
+            if not spec_path.exists():
+                spec_path = ATOMIZER_ROOT / "studies" / study_id / "1_setup" / "atomizer_spec.json"
+
+        return spec_path if spec_path.exists() else None
+
+    def _get_file_hash(self, path: Optional[Path]) -> Optional[str]:
+        """Get MD5 hash of a file for change detection."""
+        if not path or not path.exists():
+            return None
+        try:
+            with open(path, "rb") as f:
+                return hashlib.md5(f.read()).hexdigest()
+        except Exception:
+            return None
+
     async def switch_mode(
         self,
         session_id: str,
@@ -313,6 +356,7 @@ class SessionManager:
         """
         import re
         import logging
+
         logger = logging.getLogger(__name__)
 
         modifications = []
@@ -327,14 +371,16 @@ class SessionManager:
 
         try:
             # Method 1: Look for JSON in code fences
-            code_block_pattern = r'```(?:json)?\s*([\s\S]*?)```'
+            code_block_pattern = r"```(?:json)?\s*([\s\S]*?)```"
             for match in re.finditer(code_block_pattern, response):
                 block_content = match.group(1).strip()
                 try:
                     obj = json.loads(block_content)
-                    if isinstance(obj, dict) and 'modification' in obj:
-                        logger.info(f"[CANVAS_MOD] Found modification in code fence: {obj['modification']}")
-                        modifications.append(obj['modification'])
+                    if isinstance(obj, dict) and "modification" in obj:
+                        logger.info(
+                            f"[CANVAS_MOD] Found modification in code fence: {obj['modification']}"
+                        )
+                        modifications.append(obj["modification"])
                 except json.JSONDecodeError:
                     continue
 
@@ -342,7 +388,7 @@ class SessionManager:
             # This handles nested objects correctly
             i = 0
             while i < len(response):
-                if response[i] == '{':
+                if response[i] == "{":
                     # Found a potential JSON start, find matching close
                     brace_count = 1
                     j = i + 1
@@ -354,14 +400,14 @@ class SessionManager:
 
                         if escape_next:
                             escape_next = False
-                        elif char == '\\':
+                        elif char == "\\":
                             escape_next = True
                         elif char == '"' and not escape_next:
                             in_string = not in_string
                         elif not in_string:
-                            if char == '{':
+                            if char == "{":
                                 brace_count += 1
-                            elif char == '}':
+                            elif char == "}":
                                 brace_count -= 1
                         j += 1
 
@@ -369,11 +415,13 @@ class SessionManager:
                         potential_json = response[i:j]
                         try:
                             obj = json.loads(potential_json)
-                            if isinstance(obj, dict) and 'modification' in obj:
-                                mod = obj['modification']
+                            if isinstance(obj, dict) and "modification" in obj:
+                                mod = obj["modification"]
                                 # Avoid duplicates
                                 if mod not in modifications:
-                                    logger.info(f"[CANVAS_MOD] Found inline modification: action={mod.get('action')}, nodeType={mod.get('nodeType')}")
+                                    logger.info(
+                                        f"[CANVAS_MOD] Found inline modification: action={mod.get('action')}, nodeType={mod.get('nodeType')}"
+                                    )
                                     modifications.append(mod)
                         except json.JSONDecodeError as e:
                             # Not valid JSON, skip
@@ -388,6 +436,43 @@ class SessionManager:
         logger.info(f"[CANVAS_MOD] Extracted {len(modifications)} modification(s)")
         return modifications
 
+    async def _check_spec_updated(self, study_id: str) -> Optional[Dict]:
+        """
+        Check if the atomizer_spec.json was modified and return the updated spec.
+
+        For drafts in _inbox/, we check the spec file directly.
+        """
+        import logging
+
+        logger = logging.getLogger(__name__)
+
+        try:
+            # Determine spec path based on study_id
+            if study_id.startswith("draft_"):
+                spec_path = ATOMIZER_ROOT / "studies" / "_inbox" / study_id / "atomizer_spec.json"
+            else:
+                # Regular study path
+                spec_path = ATOMIZER_ROOT / "studies" / study_id / "atomizer_spec.json"
+                if not spec_path.exists():
+                    spec_path = (
+                        ATOMIZER_ROOT / "studies" / study_id / "1_setup" / "atomizer_spec.json"
+                    )
+
+            if not spec_path.exists():
+                logger.debug(f"[SPEC_CHECK] Spec not found at {spec_path}")
+                return None
+
+            # Read and return the spec
+            with open(spec_path, "r", encoding="utf-8") as f:
+                spec = json.load(f)
+
+            logger.info(f"[SPEC_CHECK] Loaded spec from {spec_path}")
+            return spec
+
+        except Exception as e:
+            logger.error(f"[SPEC_CHECK] Error checking spec: {e}")
+            return None
+
     def _build_mcp_config(self, mode: Literal["user", "power"]) -> dict:
         """Build MCP configuration for Claude"""
         return {

atomizer-dashboard/backend/api/services/spec_manager.py

@@ -47,11 +47,13 @@ from optimization_engine.config.spec_validator import (
 
 class SpecManagerError(Exception):
     """Base error for SpecManager operations."""
+
     pass
 
 
 class SpecNotFoundError(SpecManagerError):
     """Raised when spec file doesn't exist."""
+
     pass
 
 
@@ -118,7 +120,7 @@ class SpecManager:
         if not self.spec_path.exists():
             raise SpecNotFoundError(f"Spec not found: {self.spec_path}")
 
-        with open(self.spec_path, 'r', encoding='utf-8') as f:
+        with open(self.spec_path, "r", encoding="utf-8") as f:
             data = json.load(f)
 
         if validate:
@@ -141,14 +143,15 @@ class SpecManager:
         if not self.spec_path.exists():
             raise SpecNotFoundError(f"Spec not found: {self.spec_path}")
 
-        with open(self.spec_path, 'r', encoding='utf-8') as f:
+        with open(self.spec_path, "r", encoding="utf-8") as f:
             return json.load(f)
 
     def save(
         self,
         spec: Union[AtomizerSpec, Dict[str, Any]],
         modified_by: str = "api",
-        expected_hash: Optional[str] = None
+        expected_hash: Optional[str] = None,
+        skip_validation: bool = False,
     ) -> str:
         """
         Save spec with validation and broadcast.
@@ -157,6 +160,7 @@ class SpecManager:
             spec: Spec to save (AtomizerSpec or dict)
             modified_by: Who/what is making the change
             expected_hash: If provided, verify current file hash matches
+            skip_validation: If True, skip strict validation (for draft specs)
 
         Returns:
             New spec hash
@@ -167,7 +171,7 @@ class SpecManager:
         """
         # Convert to dict if needed
         if isinstance(spec, AtomizerSpec):
-            data = spec.model_dump(mode='json')
+            data = spec.model_dump(mode="json")
         else:
             data = spec
 
@@ -176,24 +180,30 @@ class SpecManager:
             current_hash = self.get_hash()
             if current_hash != expected_hash:
                 raise SpecConflictError(
-                    "Spec was modified by another client",
-                    current_hash=current_hash
+                    "Spec was modified by another client", current_hash=current_hash
                 )
 
         # Update metadata
-        now = datetime.now(timezone.utc).isoformat().replace('+00:00', 'Z')
+        now = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
         data["meta"]["modified"] = now
         data["meta"]["modified_by"] = modified_by
 
-        # Validate
-        self.validator.validate(data, strict=True)
+        # Validate (skip for draft specs or when explicitly requested)
+        status = data.get("meta", {}).get("status", "draft")
+        is_draft = status in ("draft", "introspected", "configured")
+
+        if not skip_validation and not is_draft:
+            self.validator.validate(data, strict=True)
+        elif not skip_validation:
+            # For draft specs, just validate non-strictly (collect warnings only)
+            self.validator.validate(data, strict=False)
 
         # Compute new hash
         new_hash = self._compute_hash(data)
 
         # Atomic write (write to temp, then rename)
-        temp_path = self.spec_path.with_suffix('.tmp')
-        with open(temp_path, 'w', encoding='utf-8') as f:
+        temp_path = self.spec_path.with_suffix(".tmp")
+        with open(temp_path, "w", encoding="utf-8") as f:
             json.dump(data, f, indent=2, ensure_ascii=False)
 
         temp_path.replace(self.spec_path)
@@ -202,12 +212,9 @@ class SpecManager:
         self._last_hash = new_hash
 
         # Broadcast to subscribers
-        self._broadcast({
-            "type": "spec_updated",
-            "hash": new_hash,
-            "modified_by": modified_by,
-            "timestamp": now
-        })
+        self._broadcast(
+            {"type": "spec_updated", "hash": new_hash, "modified_by": modified_by, "timestamp": now}
+        )
 
         return new_hash
 
@@ -219,7 +226,7 @@ class SpecManager:
         """Get current spec hash."""
         if not self.spec_path.exists():
             return ""
-        with open(self.spec_path, 'r', encoding='utf-8') as f:
+        with open(self.spec_path, "r", encoding="utf-8") as f:
             data = json.load(f)
         return self._compute_hash(data)
 
@@ -240,12 +247,7 @@ class SpecManager:
     # Patch Operations
     # =========================================================================
 
-    def patch(
-        self,
-        path: str,
-        value: Any,
-        modified_by: str = "api"
-    ) -> AtomizerSpec:
+    def patch(self, path: str, value: Any, modified_by: str = "api") -> AtomizerSpec:
         """
         Apply a JSONPath-style modification.
 
@@ -306,7 +308,7 @@ class SpecManager:
         """Parse JSONPath into parts."""
         # Handle both dot notation and bracket notation
         parts = []
-        for part in re.split(r'\.|\[|\]', path):
+        for part in re.split(r"\.|\[|\]", path):
             if part:
                 parts.append(part)
         return parts
@@ -316,10 +318,7 @@ class SpecManager:
     # =========================================================================
 
     def add_node(
-        self,
-        node_type: str,
-        node_data: Dict[str, Any],
-        modified_by: str = "canvas"
+        self, node_type: str, node_data: Dict[str, Any], modified_by: str = "canvas"
     ) -> str:
         """
         Add a new node (design var, extractor, objective, constraint).
@@ -353,20 +352,19 @@ class SpecManager:
         self.save(data, modified_by)
 
         # Broadcast node addition
-        self._broadcast({
-            "type": "node_added",
-            "node_type": node_type,
-            "node_id": node_id,
-            "modified_by": modified_by
-        })
+        self._broadcast(
+            {
+                "type": "node_added",
+                "node_type": node_type,
+                "node_id": node_id,
+                "modified_by": modified_by,
+            }
+        )
 
         return node_id
 
     def update_node(
-        self,
-        node_id: str,
-        updates: Dict[str, Any],
-        modified_by: str = "canvas"
+        self, node_id: str, updates: Dict[str, Any], modified_by: str = "canvas"
     ) -> None:
         """
         Update an existing node.
@@ -396,11 +394,7 @@ class SpecManager:
 
         self.save(data, modified_by)
 
-    def remove_node(
-        self,
-        node_id: str,
-        modified_by: str = "canvas"
-    ) -> None:
+    def remove_node(self, node_id: str, modified_by: str = "canvas") -> None:
         """
         Remove a node and all edges referencing it.
 
@@ -427,24 +421,18 @@ class SpecManager:
         # Remove edges referencing this node
         if "canvas" in data and data["canvas"] and "edges" in data["canvas"]:
             data["canvas"]["edges"] = [
-                e for e in data["canvas"]["edges"]
+                e
+                for e in data["canvas"]["edges"]
                 if e.get("source") != node_id and e.get("target") != node_id
             ]
 
         self.save(data, modified_by)
 
         # Broadcast node removal
-        self._broadcast({
-            "type": "node_removed",
-            "node_id": node_id,
-            "modified_by": modified_by
-        })
+        self._broadcast({"type": "node_removed", "node_id": node_id, "modified_by": modified_by})
 
     def update_node_position(
-        self,
-        node_id: str,
-        position: Dict[str, float],
-        modified_by: str = "canvas"
+        self, node_id: str, position: Dict[str, float], modified_by: str = "canvas"
     ) -> None:
         """
         Update a node's canvas position.
@@ -456,12 +444,7 @@ class SpecManager:
         """
         self.update_node(node_id, {"canvas_position": position}, modified_by)
 
-    def add_edge(
-        self,
-        source: str,
-        target: str,
-        modified_by: str = "canvas"
-    ) -> None:
+    def add_edge(self, source: str, target: str, modified_by: str = "canvas") -> None:
         """
         Add a canvas edge between nodes.
 
@@ -483,19 +466,11 @@ class SpecManager:
             if edge.get("source") == source and edge.get("target") == target:
                 return  # Already exists
 
-        data["canvas"]["edges"].append({
-            "source": source,
-            "target": target
-        })
+        data["canvas"]["edges"].append({"source": source, "target": target})
 
         self.save(data, modified_by)
 
-    def remove_edge(
-        self,
-        source: str,
-        target: str,
-        modified_by: str = "canvas"
-    ) -> None:
+    def remove_edge(self, source: str, target: str, modified_by: str = "canvas") -> None:
         """
         Remove a canvas edge.
 
@@ -508,7 +483,8 @@ class SpecManager:
 
         if "canvas" in data and data["canvas"] and "edges" in data["canvas"]:
             data["canvas"]["edges"] = [
-                e for e in data["canvas"]["edges"]
+                e
+                for e in data["canvas"]["edges"]
                 if not (e.get("source") == source and e.get("target") == target)
             ]
 
@@ -524,7 +500,7 @@ class SpecManager:
         code: str,
         outputs: List[str],
         description: Optional[str] = None,
-        modified_by: str = "claude"
+        modified_by: str = "claude",
     ) -> str:
         """
         Add a custom extractor function.
@@ -546,9 +522,7 @@ class SpecManager:
         try:
             compile(code, f"<custom:{name}>", "exec")
         except SyntaxError as e:
-            raise SpecValidationError(
-                f"Invalid Python syntax: {e.msg} at line {e.lineno}"
-            )
+            raise SpecValidationError(f"Invalid Python syntax: {e.msg} at line {e.lineno}")
 
         data = self.load_raw()
 
@@ -561,13 +535,9 @@ class SpecManager:
             "name": description or f"Custom: {name}",
             "type": "custom_function",
             "builtin": False,
-            "function": {
-                "name": name,
-                "module": "custom_extractors.dynamic",
-                "source_code": code
-            },
+            "function": {"name": name, "module": "custom_extractors.dynamic", "source_code": code},
             "outputs": [{"name": o, "metric": "custom"} for o in outputs],
-            "canvas_position": self._auto_position("extractor", data)
+            "canvas_position": self._auto_position("extractor", data),
         }
 
         data["extractors"].append(extractor)
@@ -580,7 +550,7 @@ class SpecManager:
         extractor_id: str,
         code: Optional[str] = None,
         outputs: Optional[List[str]] = None,
-        modified_by: str = "claude"
+        modified_by: str = "claude",
     ) -> None:
         """
         Update an existing custom function.
@@ -611,9 +581,7 @@ class SpecManager:
             try:
                 compile(code, f"<custom:{extractor_id}>", "exec")
             except SyntaxError as e:
-                raise SpecValidationError(
-                    f"Invalid Python syntax: {e.msg} at line {e.lineno}"
-                )
+                raise SpecValidationError(f"Invalid Python syntax: {e.msg} at line {e.lineno}")
             if "function" not in extractor:
                 extractor["function"] = {}
             extractor["function"]["source_code"] = code
@@ -672,7 +640,7 @@ class SpecManager:
             "design_variable": "dv",
             "extractor": "ext",
             "objective": "obj",
-            "constraint": "con"
+            "constraint": "con",
         }
         prefix = prefix_map.get(node_type, node_type[:3])
 
@@ -697,7 +665,7 @@ class SpecManager:
             "design_variable": "design_variables",
             "extractor": "extractors",
             "objective": "objectives",
-            "constraint": "constraints"
+            "constraint": "constraints",
         }
         return section_map.get(node_type, node_type + "s")
 
@@ -709,7 +677,7 @@ class SpecManager:
             "design_variable": 50,
             "extractor": 740,
             "objective": 1020,
-            "constraint": 1020
+            "constraint": 1020,
         }
 
         x = x_positions.get(node_type, 400)
@@ -729,11 +697,123 @@ class SpecManager:
 
         return {"x": x, "y": y}
 
+    # =========================================================================
+    # Intake Workflow Methods
+    # =========================================================================
+
+    def update_status(self, status: str, modified_by: str = "api") -> None:
+        """
+        Update the spec status field.
+
+        Args:
+            status: New status (draft, introspected, configured, validated, ready, running, completed, failed)
+            modified_by: Who/what is making the change
+        """
+        data = self.load_raw()
+        data["meta"]["status"] = status
+        self.save(data, modified_by)
+
+    def get_status(self) -> str:
+        """
+        Get the current spec status.
+
+        Returns:
+            Current status string
+        """
+        if not self.exists():
+            return "unknown"
+        data = self.load_raw()
+        return data.get("meta", {}).get("status", "draft")
+
+    def add_introspection(
+        self, introspection_data: Dict[str, Any], modified_by: str = "introspection"
+    ) -> None:
+        """
+        Add introspection data to the spec's model section.
+
+        Args:
+            introspection_data: Dict with timestamp, expressions, mass_kg, etc.
+            modified_by: Who/what is making the change
+        """
+        data = self.load_raw()
+
+        if "model" not in data:
+            data["model"] = {}
+
+        data["model"]["introspection"] = introspection_data
+        data["meta"]["status"] = "introspected"
+
+        self.save(data, modified_by)
+
+    def add_baseline(
+        self, baseline_data: Dict[str, Any], modified_by: str = "baseline_solve"
+    ) -> None:
+        """
+        Add baseline solve results to introspection data.
+
+        Args:
+            baseline_data: Dict with timestamp, solve_time_seconds, mass_kg, etc.
+            modified_by: Who/what is making the change
+        """
+        data = self.load_raw()
+
+        if "model" not in data:
+            data["model"] = {}
+        if "introspection" not in data["model"] or data["model"]["introspection"] is None:
+            data["model"]["introspection"] = {}
+
+        data["model"]["introspection"]["baseline"] = baseline_data
+
+        # Update status based on baseline success
+        if baseline_data.get("success", False):
+            data["meta"]["status"] = "validated"
+
+        self.save(data, modified_by)
+
+    def set_topic(self, topic: str, modified_by: str = "api") -> None:
+        """
+        Set the spec's topic field.
+
+        Args:
+            topic: Topic folder name
+            modified_by: Who/what is making the change
+        """
+        data = self.load_raw()
+        data["meta"]["topic"] = topic
+        self.save(data, modified_by)
+
+    def get_introspection(self) -> Optional[Dict[str, Any]]:
+        """
+        Get introspection data from spec.
+
+        Returns:
+            Introspection dict or None if not present
+        """
+        if not self.exists():
+            return None
+        data = self.load_raw()
+        return data.get("model", {}).get("introspection")
+
+    def get_design_candidates(self) -> List[Dict[str, Any]]:
+        """
+        Get expressions marked as design variable candidates.
+
+        Returns:
+            List of expression dicts where is_candidate=True
+        """
+        introspection = self.get_introspection()
+        if not introspection:
+            return []
+
+        expressions = introspection.get("expressions", [])
+        return [e for e in expressions if e.get("is_candidate", False)]
+
 
 # =========================================================================
 # Factory Function
 # =========================================================================
 
+
 def get_spec_manager(study_path: Union[str, Path]) -> SpecManager:
     """
     Get a SpecManager instance for a study.

atomizer-dashboard/frontend/src/App.tsx

@@ -9,6 +9,7 @@ import Analysis from './pages/Analysis';
 import Insights from './pages/Insights';
 import Results from './pages/Results';
 import CanvasView from './pages/CanvasView';
+import Studio from './pages/Studio';
 
 const queryClient = new QueryClient({
   defaultOptions: {
@@ -32,6 +33,10 @@ function App() {
             <Route path="canvas" element={<CanvasView />} />
             <Route path="canvas/*" element={<CanvasView />} />
 
+            {/* Studio - unified study creation environment */}
+            <Route path="studio" element={<Studio />} />
+            <Route path="studio/:draftId" element={<Studio />} />
+
             {/* Study pages - with sidebar layout */}
             <Route element={<MainLayout />}>
               <Route path="setup" element={<Setup />} />

atomizer-dashboard/frontend/src/api/intake.ts

@@ -0,0 +1,411 @@
+/**
+ * Intake API Client
+ *
+ * API client methods for the study intake workflow.
+ */
+
+import {
+  CreateInboxRequest,
+  CreateInboxResponse,
+  IntrospectRequest,
+  IntrospectResponse,
+  ListInboxResponse,
+  ListTopicsResponse,
+  InboxStudyDetail,
+  GenerateReadmeResponse,
+  FinalizeRequest,
+  FinalizeResponse,
+  UploadFilesResponse,
+} from '../types/intake';
+
+const API_BASE = '/api';
+
+/**
+ * Intake API client for study creation workflow.
+ */
+export const intakeApi = {
+  /**
+   * Create a new inbox study folder with initial spec.
+   */
+  async createInbox(request: CreateInboxRequest): Promise<CreateInboxResponse> {
+    const response = await fetch(`${API_BASE}/intake/create`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(request),
+    });
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to create inbox study');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Run NX introspection on an inbox study.
+   */
+  async introspect(request: IntrospectRequest): Promise<IntrospectResponse> {
+    const response = await fetch(`${API_BASE}/intake/introspect`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(request),
+    });
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Introspection failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * List all studies in the inbox.
+   */
+  async listInbox(): Promise<ListInboxResponse> {
+    const response = await fetch(`${API_BASE}/intake/list`);
+
+    if (!response.ok) {
+      throw new Error('Failed to fetch inbox studies');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * List existing topic folders.
+   */
+  async listTopics(): Promise<ListTopicsResponse> {
+    const response = await fetch(`${API_BASE}/intake/topics`);
+
+    if (!response.ok) {
+      throw new Error('Failed to fetch topics');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Get detailed information about an inbox study.
+   */
+  async getInboxStudy(studyName: string): Promise<InboxStudyDetail> {
+    const response = await fetch(`${API_BASE}/intake/${encodeURIComponent(studyName)}`);
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to fetch inbox study');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Delete an inbox study.
+   */
+  async deleteInboxStudy(studyName: string): Promise<{ success: boolean; deleted: string }> {
+    const response = await fetch(`${API_BASE}/intake/${encodeURIComponent(studyName)}`, {
+      method: 'DELETE',
+    });
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to delete inbox study');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Generate README for an inbox study using Claude AI.
+   */
+  async generateReadme(studyName: string): Promise<GenerateReadmeResponse> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/readme`,
+      { method: 'POST' }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'README generation failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Finalize an inbox study and move to studies directory.
+   */
+  async finalize(studyName: string, request: FinalizeRequest): Promise<FinalizeResponse> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/finalize`,
+      {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(request),
+      }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Finalization failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Upload model files to an inbox study.
+   */
+  async uploadFiles(studyName: string, files: File[]): Promise<UploadFilesResponse> {
+    const formData = new FormData();
+    files.forEach((file) => {
+      formData.append('files', file);
+    });
+
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/upload`,
+      {
+        method: 'POST',
+        body: formData,
+      }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'File upload failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Upload context files to an inbox study.
+   * Context files help Claude understand optimization goals.
+   */
+  async uploadContextFiles(studyName: string, files: File[]): Promise<UploadFilesResponse> {
+    const formData = new FormData();
+    files.forEach((file) => {
+      formData.append('files', file);
+    });
+
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/context`,
+      {
+        method: 'POST',
+        body: formData,
+      }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Context file upload failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * List context files for an inbox study.
+   */
+  async listContextFiles(studyName: string): Promise<{
+    study_name: string;
+    context_files: Array<{ name: string; path: string; size: number; extension: string }>;
+    total: number;
+  }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/context`
+    );
+
+    if (!response.ok) {
+      throw new Error('Failed to list context files');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Delete a context file from an inbox study.
+   */
+  async deleteContextFile(studyName: string, filename: string): Promise<{ success: boolean; deleted: string }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/context/${encodeURIComponent(filename)}`,
+      { method: 'DELETE' }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to delete context file');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Create design variables from selected expressions.
+   */
+  async createDesignVariables(
+    studyName: string, 
+    expressionNames: string[],
+    options?: { autoBounds?: boolean; boundFactor?: number }
+  ): Promise<{
+    success: boolean;
+    study_name: string;
+    created: Array<{
+      id: string;
+      name: string;
+      expression_name: string;
+      bounds_min: number;
+      bounds_max: number;
+      baseline: number;
+      units: string | null;
+    }>;
+    total_created: number;
+  }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/design-variables`,
+      {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          expression_names: expressionNames,
+          auto_bounds: options?.autoBounds ?? true,
+          bound_factor: options?.boundFactor ?? 0.5,
+        }),
+      }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to create design variables');
+    }
+
+    return response.json();
+  },
+
+  // ===========================================================================
+  // Studio Endpoints (Atomizer Studio - Unified Creation Environment)
+  // ===========================================================================
+
+  /**
+   * Create an anonymous draft study for Studio workflow.
+   * Returns a temporary draft_id that can be renamed during finalization.
+   */
+  async createDraft(): Promise<{
+    success: boolean;
+    draft_id: string;
+    inbox_path: string;
+    spec_path: string;
+    status: string;
+  }> {
+    const response = await fetch(`${API_BASE}/intake/draft`, {
+      method: 'POST',
+    });
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to create draft');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Get extracted text content from context files.
+   * Used for AI context injection.
+   */
+  async getContextContent(studyName: string): Promise<{
+    success: boolean;
+    study_name: string;
+    content: string;
+    files_read: Array<{
+      name: string;
+      extension: string;
+      size: number;
+      status: string;
+      characters?: number;
+      error?: string;
+    }>;
+    total_characters: number;
+  }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/context/content`
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to get context content');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Finalize a Studio draft with rename support.
+   * Enhanced version that supports renaming draft_xxx to proper names.
+   */
+  async finalizeStudio(
+    studyName: string,
+    request: {
+      topic: string;
+      newName?: string;
+      runBaseline?: boolean;
+    }
+  ): Promise<{
+    success: boolean;
+    original_name: string;
+    final_name: string;
+    final_path: string;
+    status: string;
+    baseline_success: boolean | null;
+    readme_generated: boolean;
+  }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/finalize/studio`,
+      {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          topic: request.topic,
+          new_name: request.newName,
+          run_baseline: request.runBaseline ?? false,
+        }),
+      }
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Studio finalization failed');
+    }
+
+    return response.json();
+  },
+
+  /**
+   * Get complete draft information for Studio UI.
+   * Convenience endpoint that returns everything the Studio needs.
+   */
+  async getStudioDraft(studyName: string): Promise<{
+    success: boolean;
+    draft_id: string;
+    spec: Record<string, unknown>;
+    model_files: string[];
+    context_files: string[];
+    introspection_available: boolean;
+    design_variable_count: number;
+    objective_count: number;
+  }> {
+    const response = await fetch(
+      `${API_BASE}/intake/${encodeURIComponent(studyName)}/studio`
+    );
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(error.detail || 'Failed to get studio draft');
+    }
+
+    return response.json();
+  },
+};
+
+export default intakeApi;

atomizer-dashboard/frontend/src/components/canvas/ResizeHandle.tsx

@@ -0,0 +1,67 @@
+/**
+ * ResizeHandle - Visual drag handle for resizable panels
+ * 
+ * A thin vertical bar that can be dragged to resize panels.
+ * Shows visual feedback on hover and during drag.
+ */
+
+import { memo } from 'react';
+
+interface ResizeHandleProps {
+  /** Mouse down handler to start dragging */
+  onMouseDown: (e: React.MouseEvent) => void;
+  /** Double click handler to reset size */
+  onDoubleClick?: () => void;
+  /** Whether panel is currently being dragged */
+  isDragging?: boolean;
+  /** Position of the handle ('left' or 'right' edge of the panel) */
+  position?: 'left' | 'right';
+}
+
+function ResizeHandleComponent({
+  onMouseDown,
+  onDoubleClick,
+  isDragging = false,
+  position = 'right',
+}: ResizeHandleProps) {
+  return (
+    <div
+      className={`
+        absolute top-0 bottom-0 w-1 z-30
+        cursor-col-resize
+        transition-colors duration-150
+        ${position === 'right' ? 'right-0' : 'left-0'}
+        ${isDragging 
+          ? 'bg-primary-500' 
+          : 'bg-transparent hover:bg-primary-500/50'
+        }
+      `}
+      onMouseDown={onMouseDown}
+      onDoubleClick={onDoubleClick}
+      title="Drag to resize, double-click to reset"
+    >
+      {/* Wider hit area for easier grabbing */}
+      <div 
+        className={`
+          absolute top-0 bottom-0 w-3
+          ${position === 'right' ? '-left-1' : '-right-1'}
+        `}
+      />
+      
+      {/* Visual indicator dots (shown on hover via CSS) */}
+      <div className={`
+        absolute top-1/2 -translate-y-1/2
+        ${position === 'right' ? '-left-0.5' : '-right-0.5'}
+        flex flex-col gap-1 opacity-0 hover:opacity-100 transition-opacity
+        ${isDragging ? 'opacity-100' : ''}
+      `}>
+        <div className="w-1 h-1 rounded-full bg-dark-400" />
+        <div className="w-1 h-1 rounded-full bg-dark-400" />
+        <div className="w-1 h-1 rounded-full bg-dark-400" />
+      </div>
+    </div>
+  );
+}
+
+export const ResizeHandle = memo(ResizeHandleComponent);
+export default ResizeHandle;

atomizer-dashboard/frontend/src/components/canvas/SpecRenderer.tsx

@@ -10,7 +10,8 @@
  * P2.7-P2.10: SpecRenderer component with node/edge/selection handling
  */
 
-import { useCallback, useRef, useEffect, useMemo, DragEvent } from 'react';
+import { useCallback, useRef, useEffect, useMemo, useState, DragEvent } from 'react';
+import { Play, Square, Loader2, Eye, EyeOff, CheckCircle, AlertCircle } from 'lucide-react';
 import ReactFlow, {
   Background,
   Controls,
@@ -22,6 +23,7 @@ import ReactFlow, {
   NodeChange,
   EdgeChange,
   Connection,
+  applyNodeChanges,
 } from 'reactflow';
 import 'reactflow/dist/style.css';
 
@@ -36,23 +38,34 @@ import {
   useSelectedEdgeId,
 } from '../../hooks/useSpecStore';
 import { useSpecWebSocket } from '../../hooks/useSpecWebSocket';
+import { usePanelStore } from '../../hooks/usePanelStore';
+import { useOptimizationStream } from '../../hooks/useOptimizationStream';
 import { ConnectionStatusIndicator } from './ConnectionStatusIndicator';
+import { ProgressRing } from './visualization/ConvergenceSparkline';
 import { CanvasNodeData } from '../../lib/canvas/schema';
+import { validateSpec, canRunOptimization } from '../../lib/validation/specValidator';
 
 // ============================================================================
 // Drag-Drop Helpers
 // ============================================================================
 
-/** Addable node types via drag-drop */
-const ADDABLE_NODE_TYPES = ['designVar', 'extractor', 'objective', 'constraint'] as const;
+import { SINGLETON_TYPES } from './palette/NodePalette';
+
+/** All node types that can be added via drag-drop */
+const ADDABLE_NODE_TYPES = ['model', 'solver', 'designVar', 'extractor', 'objective', 'constraint', 'algorithm', 'surrogate'] as const;
 type AddableNodeType = typeof ADDABLE_NODE_TYPES[number];
 
 function isAddableNodeType(type: string): type is AddableNodeType {
   return ADDABLE_NODE_TYPES.includes(type as AddableNodeType);
 }
 
+/** Check if a node type is a singleton (only one allowed) */
+function isSingletonType(type: string): boolean {
+  return SINGLETON_TYPES.includes(type as typeof SINGLETON_TYPES[number]);
+}
+
 /** Maps canvas NodeType to spec API type */
-function mapNodeTypeToSpecType(type: AddableNodeType): 'designVar' | 'extractor' | 'objective' | 'constraint' {
+function mapNodeTypeToSpecType(type: AddableNodeType): 'designVar' | 'extractor' | 'objective' | 'constraint' | 'model' | 'solver' | 'algorithm' | 'surrogate' {
   return type;
 }
 
@@ -61,6 +74,22 @@ function getDefaultNodeData(type: AddableNodeType, position: { x: number; y: num
   const timestamp = Date.now();
   
   switch (type) {
+    case 'model':
+      return {
+        name: 'Model',
+        sim: {
+          path: '',
+          solver: 'nastran',
+        },
+        canvas_position: position,
+      };
+    case 'solver':
+      return {
+        name: 'Solver',
+        engine: 'nxnastran',
+        solution_type: 'SOL101',
+        canvas_position: position,
+      };
     case 'designVar':
       return {
         name: `variable_${timestamp}`,
@@ -74,8 +103,28 @@ function getDefaultNodeData(type: AddableNodeType, position: { x: number; y: num
     case 'extractor':
       return {
         name: `extractor_${timestamp}`,
-        type: 'custom',
+        type: 'custom_function',  // Must be valid ExtractorType
+        builtin: false,
         enabled: true,
+        // Custom function extractors need a function definition
+        function: {
+          name: 'extract',
+          source_code: `def extract(op2_path: str, config: dict = None) -> dict:
+    """
+    Custom extractor function.
+    
+    Args:
+        op2_path: Path to the OP2 results file
+        config: Optional configuration dict
+    
+    Returns:
+        Dictionary with extracted values
+    """
+    # TODO: Implement extraction logic
+    return {'value': 0.0}
+`,
+        },
+        outputs: [{ name: 'value', metric: 'custom' }],
         canvas_position: position,
       };
     case 'objective':
@@ -83,20 +132,44 @@ function getDefaultNodeData(type: AddableNodeType, position: { x: number; y: num
         name: `objective_${timestamp}`,
         direction: 'minimize',
         weight: 1.0,
-        source_extractor_id: null,
-        source_output: null,
+        // Source is required - use placeholder that user must configure
+        source: {
+          extractor_id: 'ext_001',  // Placeholder - user needs to configure
+          output_name: 'value',
+        },
         canvas_position: position,
       };
     case 'constraint':
       return {
         name: `constraint_${timestamp}`,
-        type: 'upper',
-        limit: 1.0,
-        source_extractor_id: null,
-        source_output: null,
+        type: 'hard',  // Must be 'hard' or 'soft' (field is 'type' not 'constraint_type')
+        operator: '<=',
+        threshold: 1.0,  // Field is 'threshold' not 'limit'
+        // Source is required
+        source: {
+          extractor_id: 'ext_001',  // Placeholder - user needs to configure
+          output_name: 'value',
+        },
         enabled: true,
         canvas_position: position,
       };
+    case 'algorithm':
+      return {
+        name: 'Algorithm',
+        type: 'TPE',
+        budget: {
+          max_trials: 100,
+        },
+        canvas_position: position,
+      };
+    case 'surrogate':
+      return {
+        name: 'Surrogate',
+        enabled: false,
+        model_type: 'MLP',
+        min_trials: 20,
+        canvas_position: position,
+      };
   }
 }
 
@@ -162,6 +235,7 @@ function SpecRendererInner({
     clearSelection,
     updateNodePosition,
     addNode,
+    updateNode,
     addEdge,
     removeEdge,
     removeNode,
@@ -173,6 +247,170 @@ function SpecRendererInner({
   const wsStudyId = enableWebSocket ? storeStudyId : null;
   const { status: wsStatus } = useSpecWebSocket(wsStudyId);
 
+  // Panel store for validation and error panels
+  const { setValidationData, addError, openPanel } = usePanelStore();
+
+  // Optimization WebSocket stream for real-time updates
+  const {
+    status: optimizationStatus,
+    progress: wsProgress,
+    bestTrial: wsBestTrial,
+    recentTrials,
+  } = useOptimizationStream(studyId, {
+    autoReportErrors: true,
+    onTrialComplete: (trial) => {
+      console.log('[SpecRenderer] Trial completed:', trial.trial_number);
+    },
+    onNewBest: (best) => {
+      console.log('[SpecRenderer] New best found:', best.value);
+      setShowResults(true); // Auto-show results when new best found
+    },
+  });
+
+  // Optimization execution state
+  const isRunning = optimizationStatus === 'running';
+  const [isStarting, setIsStarting] = useState(false);
+  const [showResults, setShowResults] = useState(false);
+  const [validationStatus, setValidationStatus] = useState<'valid' | 'invalid' | 'unchecked'>('unchecked');
+
+  // When connecting Extractor → Objective/Constraint and the extractor has multiple outputs,
+  // we prompt the user to choose which output_name to use.
+  const [pendingOutputSelect, setPendingOutputSelect] = useState<null | {
+    sourceId: string;
+    targetId: string;
+    outputNames: string[];
+    selected: string;
+  }>(null);
+
+  // Build trial history for sparklines (extract objective values from recent trials)
+  const trialHistory = useMemo(() => {
+    const history: Record<string, number[]> = {};
+    for (const trial of recentTrials) {
+      // Map objective values - assumes single objective for now
+      if (trial.objective !== null) {
+        const key = 'primary';
+        if (!history[key]) history[key] = [];
+        history[key].push(trial.objective);
+      }
+      // Could also extract individual params/results for multi-objective
+    }
+    // Reverse so oldest is first (for sparkline)
+    for (const key of Object.keys(history)) {
+      history[key].reverse();
+    }
+    return history;
+  }, [recentTrials]);
+
+  // Build best trial data for node display
+  const bestTrial = useMemo((): {
+    trial_number: number;
+    objective: number;
+    design_variables: Record<string, number>;
+    results: Record<string, number>;
+  } | null => {
+    if (!wsBestTrial) return null;
+    return {
+      trial_number: wsBestTrial.trial_number,
+      objective: wsBestTrial.value,
+      design_variables: wsBestTrial.params,
+      results: { primary: wsBestTrial.value, ...wsBestTrial.params },
+    };
+  }, [wsBestTrial]);
+
+  // Note: Polling removed - now using WebSocket via useOptimizationStream hook
+  // The hook handles: status updates, best trial updates, error reporting
+
+  // Validate the spec and show results in panel
+  const handleValidate = useCallback(() => {
+    if (!spec) return;
+    
+    const result = validateSpec(spec);
+    setValidationData(result);
+    setValidationStatus(result.valid ? 'valid' : 'invalid');
+    
+    // Auto-open validation panel if there are issues
+    if (!result.valid || result.warnings.length > 0) {
+      openPanel('validation');
+    }
+    
+    return result;
+  }, [spec, setValidationData, openPanel]);
+
+  const handleRun = async () => {
+    if (!studyId || !spec) return;
+    
+    // Validate before running
+    const validation = handleValidate();
+    if (!validation || !validation.valid) {
+      // Show validation panel with errors
+      return;
+    }
+    
+    // Also do a quick sanity check
+    const { canRun, reason } = canRunOptimization(spec);
+    if (!canRun) {
+      addError({
+        type: 'config_error',
+        message: reason || 'Cannot run optimization',
+        recoverable: false,
+        suggestions: ['Check the validation panel for details'],
+        timestamp: Date.now(),
+      });
+      return;
+    }
+    
+    setIsStarting(true);
+    try {
+      const res = await fetch(`/api/optimization/studies/${studyId}/run`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ trials: spec?.optimization?.budget?.max_trials || 50 })
+      });
+      if (!res.ok) {
+        const err = await res.json();
+        throw new Error(err.detail || 'Failed to start');
+      }
+      // isRunning is now derived from WebSocket state (optimizationStatus === 'running')
+      setValidationStatus('unchecked'); // Clear validation status when running
+    } catch (e) {
+      const errorMessage = e instanceof Error ? e.message : 'Failed to start optimization';
+      setError(errorMessage);
+      
+      // Also add to error panel for persistence
+      addError({
+        type: 'system_error',
+        message: errorMessage,
+        recoverable: true,
+        suggestions: ['Check if the backend is running', 'Verify the study configuration'],
+        timestamp: Date.now(),
+      });
+    } finally {
+      setIsStarting(false);
+    }
+  };
+
+  const handleStop = async () => {
+    if (!studyId) return;
+    try {
+      const res = await fetch(`/api/optimization/studies/${studyId}/stop`, { method: 'POST' });
+      if (!res.ok) {
+        const err = await res.json().catch(() => ({}));
+        throw new Error(err.detail || 'Failed to stop');
+      }
+      // isRunning will update via WebSocket when optimization actually stops
+    } catch (e) {
+      const errorMessage = e instanceof Error ? e.message : 'Failed to stop optimization';
+      setError(errorMessage);
+      addError({
+        type: 'system_error',
+        message: errorMessage,
+        recoverable: false,
+        suggestions: ['The optimization may still be running in the background'],
+        timestamp: Date.now(),
+      });
+    }
+  };
+
   // Load spec on mount if studyId provided
   useEffect(() => {
     if (studyId) {
@@ -184,10 +422,143 @@ function SpecRendererInner({
     }
   }, [studyId, loadSpec, onStudyChange]);
 
+  // -------------------------------------------------------------------------
+  // Option A: Edge projection sync (source fields are truth)
+  // Keep canvas edges in sync when user edits objective/constraint source in panels.
+  // We only enforce Extractor -> Objective/Constraint wiring edges here.
+  // -------------------------------------------------------------------------
+
+  const isEdgeSyncingRef = useRef(false);
+
+  useEffect(() => {
+    if (!spec || !studyId) return;
+    if (isEdgeSyncingRef.current) return;
+
+    const current = spec.canvas?.edges || [];
+
+    // Compute desired extractor->objective/constraint edges from source fields
+    const desiredPairs = new Set<string>();
+
+    for (const obj of spec.objectives || []) {
+      const extractorId = obj.source?.extractor_id;
+      const outputName = obj.source?.output_name;
+      if (extractorId && outputName && extractorId !== '__UNSET__' && outputName !== '__UNSET__') {
+        desiredPairs.add(`${extractorId}__${obj.id}`);
+      }
+    }
+
+    for (const con of spec.constraints || []) {
+      const extractorId = con.source?.extractor_id;
+      const outputName = con.source?.output_name;
+      if (extractorId && outputName && extractorId !== '__UNSET__' && outputName !== '__UNSET__') {
+        desiredPairs.add(`${extractorId}__${con.id}`);
+      }
+    }
+
+    // Identify current wiring edges (ext_* -> obj_*/con_*)
+    const currentWiringPairs = new Set<string>();
+    for (const e of current) {
+      if (e.source?.startsWith('ext_') && (e.target?.startsWith('obj_') || e.target?.startsWith('con_'))) {
+        currentWiringPairs.add(`${e.source}__${e.target}`);
+      }
+    }
+
+    // Determine adds/removes
+    const toAdd: Array<{ source: string; target: string }> = [];
+    for (const key of desiredPairs) {
+      if (!currentWiringPairs.has(key)) {
+        const [source, target] = key.split('__');
+        toAdd.push({ source, target });
+      }
+    }
+
+    const toRemove: Array<{ source: string; target: string }> = [];
+    for (const key of currentWiringPairs) {
+      if (!desiredPairs.has(key)) {
+        const [source, target] = key.split('__');
+        toRemove.push({ source, target });
+      }
+    }
+
+    if (toAdd.length === 0 && toRemove.length === 0) return;
+
+    isEdgeSyncingRef.current = true;
+
+    (async () => {
+      try {
+        // Remove stale edges first
+        for (const e of toRemove) {
+          await removeEdge(e.source, e.target);
+        }
+        // Add missing edges
+        for (const e of toAdd) {
+          await addEdge(e.source, e.target);
+        }
+      } catch (err) {
+        console.error('[SpecRenderer] Edge projection sync failed:', err);
+      } finally {
+        // Small delay avoids re-entrancy storms when backend broadcasts updates
+        setTimeout(() => {
+          isEdgeSyncingRef.current = false;
+        }, 250);
+      }
+    })();
+  }, [spec, studyId, addEdge, removeEdge]);
+
   // Convert spec to ReactFlow nodes
   const nodes = useMemo(() => {
-    return specToNodes(spec);
-  }, [spec]);
+    const baseNodes = specToNodes(spec);
+    
+    // Always map nodes to include history for sparklines (even if not showing results)
+    return baseNodes.map(node => {
+      // Create a mutable copy with explicit any type for dynamic property assignment
+      // eslint-disable-next-line @typescript-eslint/no-explicit-any
+      const newData: any = { ...node.data };
+      
+      // Add history for sparklines on objective nodes
+      if (node.type === 'objective') {
+        newData.history = trialHistory['primary'] || [];
+      }
+      
+      // Map results to nodes when showing results
+      if (showResults && bestTrial) {
+        if (node.type === 'designVar' && newData.expressionName) {
+          const val = bestTrial.design_variables?.[newData.expressionName];
+          if (val !== undefined) newData.resultValue = val;
+        } else if (node.type === 'objective') {
+          const outputName = newData.outputName;
+          if (outputName && bestTrial.results?.[outputName] !== undefined) {
+            newData.resultValue = bestTrial.results[outputName];
+          }
+        } else if (node.type === 'constraint') {
+          const outputName = newData.outputName;
+          if (outputName && bestTrial.results?.[outputName] !== undefined) {
+            const val = bestTrial.results[outputName];
+            newData.resultValue = val;
+            
+            // Check feasibility
+            const op = newData.operator;
+            const threshold = newData.value;
+            if (op === '<=' && threshold !== undefined) newData.isFeasible = val <= threshold;
+            else if (op === '>=' && threshold !== undefined) newData.isFeasible = val >= threshold;
+            else if (op === '<' && threshold !== undefined) newData.isFeasible = val < threshold;
+            else if (op === '>' && threshold !== undefined) newData.isFeasible = val > threshold;
+            else if (op === '==' && threshold !== undefined) newData.isFeasible = Math.abs(val - threshold) < 1e-6;
+          }
+        } else if (node.type === 'extractor') {
+          const outputNames = newData.outputNames;
+          if (outputNames && outputNames.length > 0 && bestTrial.results) {
+            const firstOut = outputNames[0];
+            if (bestTrial.results[firstOut] !== undefined) {
+              newData.resultValue = bestTrial.results[firstOut];
+            }
+          }
+        }
+      }
+      
+      return { ...node, data: newData };
+    });
+  }, [spec, showResults, bestTrial, trialHistory]);
 
   // Convert spec to ReactFlow edges with selection styling
   const edges = useMemo(() => {
@@ -208,12 +579,23 @@ function SpecRendererInner({
     nodesRef.current = nodes;
   }, [nodes]);
 
+  // Track local node state for smooth dragging
+  const [localNodes, setLocalNodes] = useState(nodes);
+  
+  // Sync local nodes with spec-derived nodes when spec changes
+  useEffect(() => {
+    setLocalNodes(nodes);
+  }, [nodes]);
+
   // Handle node position changes
   const onNodesChange = useCallback(
     (changes: NodeChange[]) => {
       if (!editable) return;
 
-      // Handle position changes
+      // Apply changes to local state for smooth dragging
+      setLocalNodes((nds) => applyNodeChanges(changes, nds));
+
+      // Handle position changes - save to spec when drag ends
       for (const change of changes) {
         if (change.type === 'position' && change.position && change.dragging === false) {
           // Dragging ended - update spec
@@ -232,34 +614,111 @@ function SpecRendererInner({
     (changes: EdgeChange[]) => {
       if (!editable) return;
 
+      const classify = (id: string): string => {
+        if (id === 'model' || id === 'solver' || id === 'algorithm' || id === 'surrogate') return id;
+        const prefix = id.split('_')[0];
+        if (prefix === 'dv') return 'designVar';
+        if (prefix === 'ext') return 'extractor';
+        if (prefix === 'obj') return 'objective';
+        if (prefix === 'con') return 'constraint';
+        return 'unknown';
+      };
+
       for (const change of changes) {
         if (change.type === 'remove') {
           // Find the edge being removed
           const edge = edges.find((e) => e.id === change.id);
-          if (edge) {
-            removeEdge(edge.source, edge.target).catch((err) => {
-              console.error('Failed to remove edge:', err);
+          if (!edge) continue;
+
+          const sourceType = classify(edge.source);
+          const targetType = classify(edge.target);
+
+          // First remove the visual edge
+          removeEdge(edge.source, edge.target).catch((err) => {
+            console.error('Failed to remove edge:', err);
+            setError(err.message);
+          });
+
+          // Option A truth model: if we removed Extractor -> Objective/Constraint,
+          // clear the target's source to avoid stale runnable config.
+          if (sourceType === 'extractor' && (targetType === 'objective' || targetType === 'constraint')) {
+            updateNode(edge.target, {
+              // Objective/constraint.source is required by schema.
+              // Use explicit UNSET placeholders so validation can catch it
+              // without risking accidental execution.
+              source: { extractor_id: '__UNSET__', output_name: '__UNSET__' },
+            }).catch((err) => {
+              console.error('Failed to clear source on node:', err);
               setError(err.message);
             });
           }
         }
       }
     },
-    [editable, edges, removeEdge, setError]
+    [editable, edges, removeEdge, setError, updateNode]
   );
 
   // Handle new connections
   const onConnect = useCallback(
-    (connection: Connection) => {
+    async (connection: Connection) => {
       if (!editable) return;
       if (!connection.source || !connection.target) return;
 
-      addEdge(connection.source, connection.target).catch((err) => {
-        console.error('Failed to add edge:', err);
-        setError(err.message);
-      });
+      const sourceId = connection.source;
+      const targetId = connection.target;
+
+      // Helper: classify nodes by ID (synthetic vs spec-backed)
+      const classify = (id: string): string => {
+        if (id === 'model' || id === 'solver' || id === 'algorithm' || id === 'surrogate') return id;
+        const prefix = id.split('_')[0];
+        if (prefix === 'dv') return 'designVar';
+        if (prefix === 'ext') return 'extractor';
+        if (prefix === 'obj') return 'objective';
+        if (prefix === 'con') return 'constraint';
+        return 'unknown';
+      };
+
+      const sourceType = classify(sourceId);
+      const targetType = classify(targetId);
+
+      try {
+        // Option A truth model: objective/constraint source is the real linkage.
+        // When user connects Extractor -> Objective/Constraint, we must choose an output_name.
+        if (spec && sourceType === 'extractor' && (targetType === 'objective' || targetType === 'constraint')) {
+          const ext = spec.extractors.find((e) => e.id === sourceId);
+          const outputNames = (ext?.outputs || []).map((o) => o.name).filter(Boolean);
+
+          // If extractor has multiple outputs, prompt the user.
+          if (outputNames.length > 1) {
+            const preferred = outputNames.includes('value') ? 'value' : outputNames[0];
+            setPendingOutputSelect({
+              sourceId,
+              targetId,
+              outputNames,
+              selected: preferred,
+            });
+            return;
+          }
+
+          // Single (or zero) output: choose deterministically.
+          const outputName = outputNames[0] || 'value';
+
+          // Persist edge + runnable source.
+          await addEdge(sourceId, targetId);
+          await updateNode(targetId, {
+            source: { extractor_id: sourceId, output_name: outputName },
+          });
+          return;
+        }
+
+        // Default: just persist the visual edge.
+        await addEdge(sourceId, targetId);
+      } catch (err) {
+        console.error('Failed to add connection:', err);
+        setError(err instanceof Error ? err.message : 'Failed to add connection');
+      }
     },
-    [editable, addEdge, setError]
+    [editable, addEdge, setError, spec, updateNode, setPendingOutputSelect]
   );
 
   // Handle node clicks for selection
@@ -353,6 +812,18 @@ function SpecRendererInner({
         return;
       }
 
+      // Check if this is a singleton type that already exists
+      if (isSingletonType(type)) {
+        const existingNode = localNodes.find(n => n.type === type);
+        if (existingNode) {
+          // Select the existing node instead of creating a duplicate
+          selectNode(existingNode.id);
+          // Show a toast notification would be nice here
+          console.log(`${type} already exists - selected existing node`);
+          return;
+        }
+      }
+
       // Convert screen position to flow position
       const position = reactFlowInstance.current.screenToFlowPosition({
         x: event.clientX,
@@ -363,8 +834,19 @@ function SpecRendererInner({
       const nodeData = getDefaultNodeData(type, position);
       const specType = mapNodeTypeToSpecType(type);
 
+      // For structural types (model, solver, algorithm, surrogate), these are 
+      // part of the spec structure rather than array items. Handle differently.
+      const structuralTypes = ['model', 'solver', 'algorithm', 'surrogate'];
+      if (structuralTypes.includes(type)) {
+        // These nodes are derived from spec structure - they shouldn't be "added"
+        // They already exist if the spec has that section configured
+        console.log(`${type} is a structural node - configure via spec directly`);
+        setError(`${type} nodes are configured via the spec. Use the config panel to edit.`);
+        return;
+      }
+
       try {
-        const nodeId = await addNode(specType, nodeData);
+        const nodeId = await addNode(specType as 'designVar' | 'extractor' | 'objective' | 'constraint', nodeData);
         // Select the newly created node
         selectNode(nodeId);
       } catch (err) {
@@ -372,9 +854,37 @@ function SpecRendererInner({
         setError(err instanceof Error ? err.message : 'Failed to add node');
       }
     },
-    [editable, addNode, selectNode, setError]
+    [editable, addNode, selectNode, setError, localNodes]
   );
 
+  // -------------------------------------------------------------------------
+  // Output selection modal handlers (Extractor → Objective/Constraint)
+  // -------------------------------------------------------------------------
+
+  const confirmOutputSelection = useCallback(async () => {
+    if (!pendingOutputSelect) return;
+
+    const { sourceId, targetId, selected } = pendingOutputSelect;
+
+    try {
+      // Persist edge + runnable source wiring
+      await addEdge(sourceId, targetId);
+      await updateNode(targetId, {
+        source: { extractor_id: sourceId, output_name: selected },
+      });
+    } catch (err) {
+      console.error('Failed to apply output selection:', err);
+      setError(err instanceof Error ? err.message : 'Failed to apply output selection');
+    } finally {
+      setPendingOutputSelect(null);
+    }
+  }, [pendingOutputSelect, addEdge, updateNode, setError]);
+
+  const cancelOutputSelection = useCallback(() => {
+    // User canceled: do not create the edge, do not update source
+    setPendingOutputSelect(null);
+  }, []);
+
   // Loading state
   if (showLoadingOverlay && isLoading && !spec) {
     return (
@@ -457,14 +967,65 @@ function SpecRendererInner({
         </div>
       )}
 
+      {/* Output selection modal (Extractor → Objective/Constraint) */}
+      {pendingOutputSelect && (
+        <div className="absolute inset-0 z-30 flex items-center justify-center bg-black/60 backdrop-blur-sm">
+          <div className="w-[520px] max-w-[90vw] bg-dark-850 border border-dark-600 rounded-xl shadow-2xl p-5">
+            <h3 className="text-white font-semibold text-lg">Select extractor output</h3>
+            <p className="text-sm text-dark-300 mt-1">
+              This extractor provides multiple outputs. Choose which output the target should use.
+            </p>
+
+            <div className="mt-4">
+              <label className="block text-sm font-medium text-dark-300 mb-1">Output</label>
+              <select
+                value={pendingOutputSelect.selected}
+                onChange={(e) =>
+                  setPendingOutputSelect((prev) =>
+                    prev ? { ...prev, selected: e.target.value } : prev
+                  )
+                }
+                className="w-full px-3 py-2 bg-dark-800 border border-dark-600 text-white rounded-lg focus:border-primary-500 focus:outline-none transition-colors"
+              >
+                {pendingOutputSelect.outputNames.map((name) => (
+                  <option key={name} value={name}>
+                    {name}
+                  </option>
+                ))}
+              </select>
+              <p className="text-xs text-dark-500 mt-2">
+                Tip: we default to <span className="text-dark-300 font-medium">value</span> when available.
+              </p>
+            </div>
+
+            <div className="mt-5 flex justify-end gap-2">
+              <button
+                onClick={cancelOutputSelection}
+                className="px-4 py-2 bg-dark-700 text-dark-200 hover:bg-dark-600 rounded-lg border border-dark-600 transition-colors"
+              >
+                Cancel
+              </button>
+              <button
+                onClick={confirmOutputSelection}
+                className="px-4 py-2 bg-primary-600 text-white hover:bg-primary-500 rounded-lg border border-primary-500 transition-colors"
+              >
+                Connect
+              </button>
+            </div>
+          </div>
+        </div>
+      )}
+
       <ReactFlow
-        nodes={nodes}
+        nodes={localNodes}
         edges={edges}
         onNodesChange={onNodesChange}
         onEdgesChange={onEdgesChange}
         onConnect={onConnect}
         onInit={(instance) => {
           reactFlowInstance.current = instance;
+          // Auto-fit view on init with padding
+          setTimeout(() => instance.fitView({ padding: 0.2, duration: 300 }), 100);
         }}
         onDragOver={onDragOver}
         onDrop={onDrop}
@@ -473,6 +1034,7 @@ function SpecRendererInner({
         onPaneClick={onPaneClick}
         nodeTypes={nodeTypes}
         fitView
+        fitViewOptions={{ padding: 0.2, includeHiddenNodes: false }}
         deleteKeyCode={null} // We handle delete ourselves
         nodesDraggable={editable}
         nodesConnectable={editable}
@@ -488,10 +1050,113 @@ function SpecRendererInner({
         />
       </ReactFlow>
 
+      {/* Action Buttons */}
+      <div className="absolute bottom-4 right-4 z-10 flex gap-2">
+        {/* Results toggle */}
+        {bestTrial && (
+          <button
+            onClick={() => setShowResults(!showResults)}
+            className={`flex items-center gap-2 px-3 py-2 rounded-lg transition-colors border ${
+              showResults
+                ? 'bg-primary-600/90 text-white border-primary-500 hover:bg-primary-500'
+                : 'bg-dark-800 text-dark-300 border-dark-600 hover:text-white hover:border-dark-500'
+            }`}
+            title={showResults ? "Hide Results" : "Show Best Trial Results"}
+          >
+            {showResults ? <Eye size={16} /> : <EyeOff size={16} />}
+            <span className="text-sm font-medium">Results</span>
+          </button>
+        )}
+
+        {/* Validate button - shows validation status */}
+        <button
+          onClick={handleValidate}
+          className={`flex items-center gap-2 px-3 py-2 rounded-lg transition-colors border ${
+            validationStatus === 'valid'
+              ? 'bg-green-600/20 text-green-400 border-green-500/50 hover:bg-green-600/30'
+              : validationStatus === 'invalid'
+              ? 'bg-red-600/20 text-red-400 border-red-500/50 hover:bg-red-600/30'
+              : 'bg-dark-800 text-dark-300 border-dark-600 hover:text-white hover:border-dark-500'
+          }`}
+          title="Validate spec before running"
+        >
+          {validationStatus === 'valid' ? (
+            <CheckCircle size={16} />
+          ) : validationStatus === 'invalid' ? (
+            <AlertCircle size={16} />
+          ) : (
+            <CheckCircle size={16} />
+          )}
+          <span className="text-sm font-medium">Validate</span>
+        </button>
+
+        {/* Run/Stop button */}
+        {isRunning ? (
+          <button
+            onClick={handleStop}
+            className="flex items-center gap-2 px-4 py-2 bg-red-600 text-white rounded-lg hover:bg-red-500 shadow-lg transition-colors font-medium"
+          >
+            <Square size={16} fill="currentColor" />
+            Stop
+          </button>
+        ) : (
+          <button
+            onClick={handleRun}
+            disabled={isStarting || validationStatus === 'invalid'}
+            className={`flex items-center gap-2 px-4 py-2 rounded-lg shadow-lg transition-colors font-medium ${
+              validationStatus === 'invalid'
+                ? 'bg-dark-700 text-dark-400 cursor-not-allowed'
+                : 'bg-emerald-600 text-white hover:bg-emerald-500 disabled:opacity-50 disabled:cursor-not-allowed'
+            }`}
+            title={validationStatus === 'invalid' ? 'Fix validation errors first' : 'Start optimization'}
+          >
+            {isStarting ? (
+              <Loader2 size={16} className="animate-spin" />
+            ) : (
+              <Play size={16} fill="currentColor" />
+            )}
+            Run
+          </button>
+        )}
+      </div>
+
       {/* Study name badge */}
       <div className="absolute bottom-4 left-4 z-10 px-3 py-1.5 bg-dark-800/90 backdrop-blur rounded-lg border border-dark-600">
         <span className="text-sm text-dark-300">{spec.meta.study_name}</span>
       </div>
+
+      {/* Progress indicator when running */}
+      {isRunning && wsProgress && (
+        <div className="absolute bottom-4 left-1/2 -translate-x-1/2 z-10 flex items-center gap-3 px-4 py-2 bg-dark-800/95 backdrop-blur rounded-lg border border-dark-600 shadow-lg">
+          <ProgressRing 
+            progress={wsProgress.percentage} 
+            size={36} 
+            strokeWidth={3}
+            color="#10b981"
+          />
+          <div className="flex flex-col">
+            <span className="text-sm font-medium text-white">
+              Trial {wsProgress.current} / {wsProgress.total}
+            </span>
+            <span className="text-xs text-dark-400">
+              {wsProgress.fea_count > 0 && `${wsProgress.fea_count} FEA`}
+              {wsProgress.fea_count > 0 && wsProgress.nn_count > 0 && ' + '}
+              {wsProgress.nn_count > 0 && `${wsProgress.nn_count} NN`}
+              {wsProgress.fea_count === 0 && wsProgress.nn_count === 0 && 'Running...'}
+            </span>
+          </div>
+          {wsBestTrial && (
+            <div className="flex flex-col border-l border-dark-600 pl-3 ml-1">
+              <span className="text-xs text-dark-400">Best</span>
+              <span className="text-sm font-medium text-emerald-400">
+                {typeof wsBestTrial.value === 'number' 
+                  ? wsBestTrial.value.toFixed(4) 
+                  : wsBestTrial.value}
+              </span>
+            </div>
+          )}
+        </div>
+      )}
     </div>
   );
 }

atomizer-dashboard/frontend/src/components/canvas/nodes/ConstraintNode.tsx

@@ -2,12 +2,14 @@ import { memo } from 'react';
 import { NodeProps } from 'reactflow';
 import { ShieldAlert } from 'lucide-react';
 import { BaseNode } from './BaseNode';
+import { ResultBadge } from './ResultBadge';
 import { ConstraintNodeData } from '../../../lib/canvas/schema';
 
 function ConstraintNodeComponent(props: NodeProps<ConstraintNodeData>) {
   const { data } = props;
   return (
     <BaseNode {...props} icon={<ShieldAlert size={16} />} iconColor="text-amber-400">
+      <ResultBadge value={data.resultValue} isFeasible={data.isFeasible} />
       {data.name && data.operator && data.value !== undefined
         ? `${data.name} ${data.operator} ${data.value}`
         : 'Set constraint'}

atomizer-dashboard/frontend/src/components/canvas/nodes/DesignVarNode.tsx

@@ -2,12 +2,14 @@ import { memo } from 'react';
 import { NodeProps } from 'reactflow';
 import { SlidersHorizontal } from 'lucide-react';
 import { BaseNode } from './BaseNode';
+import { ResultBadge } from './ResultBadge';
 import { DesignVarNodeData } from '../../../lib/canvas/schema';
 
 function DesignVarNodeComponent(props: NodeProps<DesignVarNodeData>) {
   const { data } = props;
   return (
     <BaseNode {...props} icon={<SlidersHorizontal size={16} />} iconColor="text-emerald-400" inputs={0} outputs={1}>
+      <ResultBadge value={data.resultValue} unit={data.unit} />
       {data.expressionName ? (
         <span className="font-mono">{data.expressionName}</span>
       ) : (

atomizer-dashboard/frontend/src/components/canvas/nodes/ExtractorNode.tsx

@@ -2,12 +2,14 @@ import { memo } from 'react';
 import { NodeProps } from 'reactflow';
 import { FlaskConical } from 'lucide-react';
 import { BaseNode } from './BaseNode';
+import { ResultBadge } from './ResultBadge';
 import { ExtractorNodeData } from '../../../lib/canvas/schema';
 
 function ExtractorNodeComponent(props: NodeProps<ExtractorNodeData>) {
   const { data } = props;
   return (
     <BaseNode {...props} icon={<FlaskConical size={16} />} iconColor="text-cyan-400">
+      <ResultBadge value={data.resultValue} />
       {data.extractorName || 'Select extractor'}
     </BaseNode>
   );

atomizer-dashboard/frontend/src/components/canvas/nodes/ObjectiveNode.tsx

@@ -2,13 +2,38 @@ import { memo } from 'react';
 import { NodeProps } from 'reactflow';
 import { Target } from 'lucide-react';
 import { BaseNode } from './BaseNode';
+import { ResultBadge } from './ResultBadge';
+import { ConvergenceSparkline } from '../visualization/ConvergenceSparkline';
 import { ObjectiveNodeData } from '../../../lib/canvas/schema';
 
 function ObjectiveNodeComponent(props: NodeProps<ObjectiveNodeData>) {
   const { data } = props;
+  const hasHistory = data.history && data.history.length > 1;
+  
   return (
     <BaseNode {...props} icon={<Target size={16} />} iconColor="text-rose-400">
-      {data.name ? `${data.direction === 'maximize' ? '↑' : '↓'} ${data.name}` : 'Set objective'}
+      <div className="flex flex-col gap-1">
+        <div className="flex items-center justify-between">
+          <span className="text-sm">
+            {data.name ? `${data.direction === 'maximize' ? '↑' : '↓'} ${data.name}` : 'Set objective'}
+          </span>
+          <ResultBadge value={data.resultValue} label="Best" />
+        </div>
+        
+        {/* Convergence sparkline */}
+        {hasHistory && (
+          <div className="mt-1 -mb-1">
+            <ConvergenceSparkline 
+              values={data.history!}
+              width={120}
+              height={20}
+              direction={data.direction || 'minimize'}
+              color={data.direction === 'maximize' ? '#34d399' : '#60a5fa'}
+              showBest={true}
+            />
+          </div>
+        )}
+      </div>
     </BaseNode>
   );
 }

atomizer-dashboard/frontend/src/components/canvas/nodes/ResultBadge.tsx

@@ -0,0 +1,39 @@
+import { memo } from 'react';
+
+interface ResultBadgeProps {
+  value: number | string | null | undefined;
+  unit?: string;
+  isFeasible?: boolean; // For constraints
+  label?: string;
+}
+
+export const ResultBadge = memo(function ResultBadge({ value, unit, isFeasible, label }: ResultBadgeProps) {
+  if (value === null || value === undefined) return null;
+
+  const displayValue = typeof value === 'number' 
+    ? value.toLocaleString(undefined, { maximumFractionDigits: 4 }) 
+    : value;
+
+  // Determine color based on feasibility (if provided)
+  let bgColor = 'bg-primary-500/20';
+  let textColor = 'text-primary-300';
+  let borderColor = 'border-primary-500/30';
+
+  if (isFeasible === true) {
+    bgColor = 'bg-emerald-500/20';
+    textColor = 'text-emerald-300';
+    borderColor = 'border-emerald-500/30';
+  } else if (isFeasible === false) {
+    bgColor = 'bg-red-500/20';
+    textColor = 'text-red-300';
+    borderColor = 'border-red-500/30';
+  }
+
+  return (
+    <div className={`absolute -top-3 -right-2 px-2 py-0.5 rounded-full border ${bgColor} ${borderColor} ${textColor} text-xs font-mono shadow-lg backdrop-blur-sm z-10 flex items-center gap-1`}>
+      {label && <span className="opacity-70 mr-1">{label}:</span>}
+      <span className="font-bold">{displayValue}</span>
+      {unit && <span className="opacity-70 text-[10px] ml-0.5">{unit}</span>}
+    </div>
+  );
+});

atomizer-dashboard/frontend/src/components/canvas/nodes/SolverNode.tsx

@@ -1,14 +1,44 @@
 import { memo } from 'react';
 import { NodeProps } from 'reactflow';
-import { Cpu } from 'lucide-react';
+import { Cpu, Terminal } from 'lucide-react';
 import { BaseNode } from './BaseNode';
-import { SolverNodeData } from '../../../lib/canvas/schema';
+import { SolverNodeData, SolverEngine } from '../../../lib/canvas/schema';
+
+// Human-readable engine names
+const ENGINE_LABELS: Record<SolverEngine, string> = {
+  nxnastran: 'NX Nastran',
+  mscnastran: 'MSC Nastran',
+  python: 'Python Script',
+  abaqus: 'Abaqus',
+  ansys: 'ANSYS',
+};
 
 function SolverNodeComponent(props: NodeProps<SolverNodeData>) {
   const { data } = props;
+  
+  // Build display string: "Engine - SolutionType" or just one
+  const engineLabel = data.engine ? ENGINE_LABELS[data.engine] : null;
+  const solverTypeLabel = data.solverType || null;
+  
+  let displayText: string;
+  if (engineLabel && solverTypeLabel) {
+    displayText = `${engineLabel} (${solverTypeLabel})`;
+  } else if (engineLabel) {
+    displayText = engineLabel;
+  } else if (solverTypeLabel) {
+    displayText = solverTypeLabel;
+  } else {
+    displayText = 'Configure solver';
+  }
+  
+  // Use Terminal icon for Python, Cpu for others
+  const icon = data.engine === 'python' 
+    ? <Terminal size={16} /> 
+    : <Cpu size={16} />;
+  
   return (
-    <BaseNode {...props} icon={<Cpu size={16} />} iconColor="text-violet-400">
-      {data.solverType || 'Select solution'}
+    <BaseNode {...props} icon={icon} iconColor="text-violet-400">
+      {displayText}
     </BaseNode>
   );
 }

atomizer-dashboard/frontend/src/components/canvas/palette/NodePalette.tsx

@@ -54,6 +54,9 @@ export interface NodePaletteProps {
 // Constants
 // ============================================================================
 
+/** Singleton node types - only one of each allowed on canvas */
+export const SINGLETON_TYPES: NodeType[] = ['model', 'solver', 'algorithm', 'surrogate'];
+
 export const PALETTE_ITEMS: PaletteItem[] = [
   { 
     type: 'model', 
@@ -61,15 +64,15 @@ export const PALETTE_ITEMS: PaletteItem[] = [
     icon: Box, 
     description: 'NX model file (.prt, .sim)', 
     color: 'text-blue-400',
-    canAdd: false, // Synthetic - derived from spec
+    canAdd: true, // Singleton - only one allowed
   },
   { 
     type: 'solver', 
     label: 'Solver', 
     icon: Cpu, 
-    description: 'Nastran solution type', 
+    description: 'Analysis solver config', 
     color: 'text-violet-400',
-    canAdd: false, // Synthetic - derived from model
+    canAdd: true, // Singleton - only one allowed
   },
   { 
     type: 'designVar', 
@@ -109,7 +112,7 @@ export const PALETTE_ITEMS: PaletteItem[] = [
     icon: BrainCircuit, 
     description: 'Optimization method', 
     color: 'text-indigo-400',
-    canAdd: false, // Synthetic - derived from spec.optimization
+    canAdd: true, // Singleton - only one allowed
   },
   { 
     type: 'surrogate', 
@@ -117,7 +120,7 @@ export const PALETTE_ITEMS: PaletteItem[] = [
     icon: Rocket, 
     description: 'Neural acceleration', 
     color: 'text-pink-400',
-    canAdd: false, // Synthetic - derived from spec.optimization.surrogate
+    canAdd: true, // Singleton - only one allowed
   },
 ];
 

atomizer-dashboard/frontend/src/components/canvas/panels/ErrorPanel.tsx

@@ -0,0 +1,255 @@
+/**
+ * ErrorPanel - Displays optimization errors with recovery options
+ * 
+ * Shows errors that occurred during optimization with:
+ * - Error classification (NX crash, solver failure, etc.)
+ * - Recovery suggestions
+ * - Ability to dismiss individual errors
+ * - Support for multiple simultaneous errors
+ */
+
+import { useMemo } from 'react';
+import {
+  X,
+  AlertTriangle,
+  AlertOctagon,
+  RefreshCw,
+  Minimize2,
+  Maximize2,
+  Trash2,
+  Bug,
+  Cpu,
+  FileWarning,
+  Settings,
+  Server,
+} from 'lucide-react';
+import { useErrorPanel, usePanelStore, OptimizationError } from '../../../hooks/usePanelStore';
+
+interface ErrorPanelProps {
+  onClose: () => void;
+  onRetry?: (trial?: number) => void;
+  onSkipTrial?: (trial: number) => void;
+}
+
+export function ErrorPanel({ onClose, onRetry, onSkipTrial }: ErrorPanelProps) {
+  const panel = useErrorPanel();
+  const { minimizePanel, dismissError, clearErrors } = usePanelStore();
+
+  const sortedErrors = useMemo(() => {
+    return [...panel.errors].sort((a, b) => b.timestamp - a.timestamp);
+  }, [panel.errors]);
+
+  if (!panel.open || panel.errors.length === 0) return null;
+
+  // Minimized view
+  if (panel.minimized) {
+    return (
+      <div 
+        className="bg-dark-850 border border-red-500/50 rounded-lg shadow-xl flex items-center gap-2 px-3 py-2 cursor-pointer hover:bg-dark-800 transition-colors"
+        onClick={() => minimizePanel('error')}
+      >
+        <AlertOctagon size={16} className="text-red-400" />
+        <span className="text-sm text-white font-medium">
+          {panel.errors.length} Error{panel.errors.length !== 1 ? 's' : ''}
+        </span>
+        <Maximize2 size={14} className="text-dark-400" />
+      </div>
+    );
+  }
+
+  return (
+    <div className="bg-dark-850 border border-red-500/30 rounded-xl w-[420px] max-h-[500px] flex flex-col shadow-xl">
+      {/* Header */}
+      <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700 bg-red-500/5">
+        <div className="flex items-center gap-2">
+          <AlertOctagon size={18} className="text-red-400" />
+          <span className="font-medium text-white">
+            Optimization Errors ({panel.errors.length})
+          </span>
+        </div>
+        <div className="flex items-center gap-1">
+          {panel.errors.length > 1 && (
+            <button
+              onClick={clearErrors}
+              className="p-1.5 text-dark-400 hover:text-red-400 hover:bg-red-500/10 rounded transition-colors"
+              title="Clear all errors"
+            >
+              <Trash2 size={14} />
+            </button>
+          )}
+          <button
+            onClick={() => minimizePanel('error')}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+            title="Minimize"
+          >
+            <Minimize2 size={14} />
+          </button>
+          <button
+            onClick={onClose}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+          >
+            <X size={14} />
+          </button>
+        </div>
+      </div>
+
+      {/* Content */}
+      <div className="flex-1 overflow-y-auto p-3 space-y-3">
+        {sortedErrors.map((error) => (
+          <ErrorItem
+            key={error.timestamp}
+            error={error}
+            onDismiss={() => dismissError(error.timestamp)}
+            onRetry={onRetry}
+            onSkipTrial={onSkipTrial}
+          />
+        ))}
+      </div>
+    </div>
+  );
+}
+
+// ============================================================================
+// Error Item Component
+// ============================================================================
+
+interface ErrorItemProps {
+  error: OptimizationError;
+  onDismiss: () => void;
+  onRetry?: (trial?: number) => void;
+  onSkipTrial?: (trial: number) => void;
+}
+
+function ErrorItem({ error, onDismiss, onRetry, onSkipTrial }: ErrorItemProps) {
+  const icon = getErrorIcon(error.type);
+  const typeLabel = getErrorTypeLabel(error.type);
+  const timeAgo = getTimeAgo(error.timestamp);
+
+  return (
+    <div className="bg-dark-800 rounded-lg border border-dark-700 overflow-hidden">
+      {/* Error header */}
+      <div className="flex items-start gap-3 p-3">
+        <div className="p-2 bg-red-500/10 rounded-lg flex-shrink-0">
+          {icon}
+        </div>
+        <div className="flex-1 min-w-0">
+          <div className="flex items-center gap-2 mb-1">
+            <span className="text-xs font-medium text-red-400 uppercase tracking-wide">
+              {typeLabel}
+            </span>
+            {error.trial !== undefined && (
+              <span className="text-xs text-dark-500">
+                Trial #{error.trial}
+              </span>
+            )}
+            <span className="text-xs text-dark-600 ml-auto">
+              {timeAgo}
+            </span>
+          </div>
+          <p className="text-sm text-white">{error.message}</p>
+          {error.details && (
+            <p className="text-xs text-dark-400 mt-1 font-mono bg-dark-900 p-2 rounded mt-2 max-h-20 overflow-y-auto">
+              {error.details}
+            </p>
+          )}
+        </div>
+        <button
+          onClick={onDismiss}
+          className="p-1 text-dark-500 hover:text-white hover:bg-dark-700 rounded transition-colors flex-shrink-0"
+          title="Dismiss"
+        >
+          <X size={14} />
+        </button>
+      </div>
+
+      {/* Suggestions */}
+      {error.suggestions.length > 0 && (
+        <div className="px-3 pb-3">
+          <p className="text-xs text-dark-500 mb-1.5">Suggestions:</p>
+          <ul className="text-xs text-dark-300 space-y-1">
+            {error.suggestions.map((suggestion, idx) => (
+              <li key={idx} className="flex items-start gap-1.5">
+                <span className="text-dark-500">-</span>
+                <span>{suggestion}</span>
+              </li>
+            ))}
+          </ul>
+        </div>
+      )}
+
+      {/* Actions */}
+      {error.recoverable && (
+        <div className="flex items-center gap-2 px-3 pb-3">
+          {onRetry && (
+            <button
+              onClick={() => onRetry(error.trial)}
+              className="flex items-center gap-1.5 px-3 py-1.5 bg-primary-600 hover:bg-primary-500 
+                         text-white text-xs font-medium rounded transition-colors"
+            >
+              <RefreshCw size={12} />
+              Retry{error.trial !== undefined ? ' Trial' : ''}
+            </button>
+          )}
+          {onSkipTrial && error.trial !== undefined && (
+            <button
+              onClick={() => onSkipTrial(error.trial!)}
+              className="flex items-center gap-1.5 px-3 py-1.5 bg-dark-700 hover:bg-dark-600 
+                         text-dark-200 text-xs font-medium rounded transition-colors"
+            >
+              Skip Trial
+            </button>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
+
+// ============================================================================
+// Helper Functions
+// ============================================================================
+
+function getErrorIcon(type: OptimizationError['type']) {
+  switch (type) {
+    case 'nx_crash':
+      return <Cpu size={16} className="text-red-400" />;
+    case 'solver_fail':
+      return <AlertTriangle size={16} className="text-amber-400" />;
+    case 'extractor_error':
+      return <FileWarning size={16} className="text-orange-400" />;
+    case 'config_error':
+      return <Settings size={16} className="text-blue-400" />;
+    case 'system_error':
+      return <Server size={16} className="text-purple-400" />;
+    default:
+      return <Bug size={16} className="text-red-400" />;
+  }
+}
+
+function getErrorTypeLabel(type: OptimizationError['type']) {
+  switch (type) {
+    case 'nx_crash':
+      return 'NX Crash';
+    case 'solver_fail':
+      return 'Solver Failure';
+    case 'extractor_error':
+      return 'Extractor Error';
+    case 'config_error':
+      return 'Configuration Error';
+    case 'system_error':
+      return 'System Error';
+    default:
+      return 'Unknown Error';
+  }
+}
+
+function getTimeAgo(timestamp: number): string {
+  const seconds = Math.floor((Date.now() - timestamp) / 1000);
+  
+  if (seconds < 60) return 'just now';
+  if (seconds < 3600) return `${Math.floor(seconds / 60)}m ago`;
+  if (seconds < 86400) return `${Math.floor(seconds / 3600)}h ago`;
+  return `${Math.floor(seconds / 86400)}d ago`;
+}
+
+export default ErrorPanel;

atomizer-dashboard/frontend/src/components/canvas/panels/FloatingIntrospectionPanel.tsx

@@ -0,0 +1,485 @@
+/**
+ * FloatingIntrospectionPanel - Persistent introspection panel using store
+ * 
+ * This is a wrapper around the existing IntrospectionPanel that:
+ * 1. Gets its state from usePanelStore instead of local state
+ * 2. Persists data when the panel is closed and reopened
+ * 3. Can be opened from anywhere without losing state
+ */
+
+import { useState, useEffect, useCallback, useMemo } from 'react';
+import {
+  X,
+  Search,
+  RefreshCw,
+  Plus,
+  ChevronDown,
+  ChevronRight,
+  Cpu,
+  SlidersHorizontal,
+  Scale,
+  Minimize2,
+  Maximize2,
+} from 'lucide-react';
+import { 
+  useIntrospectionPanel, 
+  usePanelStore,
+} from '../../../hooks/usePanelStore';
+import { useSpecStore } from '../../../hooks/useSpecStore';
+
+interface FloatingIntrospectionPanelProps {
+  onClose: () => void;
+}
+
+// Reuse types from original IntrospectionPanel
+interface Expression {
+  name: string;
+  value: number;
+  rhs?: string;
+  min?: number;
+  max?: number;
+  unit?: string;
+  units?: string;
+  type: string;
+  source?: string;
+}
+
+interface ExpressionsResult {
+  user: Expression[];
+  internal: Expression[];
+  total_count: number;
+  user_count: number;
+}
+
+interface IntrospectionResult {
+  solver_type?: string;
+  expressions?: ExpressionsResult;
+  // Allow other properties from the API response
+  file_deps?: unknown[];
+  fea_results?: unknown[];
+  fem_mesh?: unknown;
+  sim_solutions?: unknown[];
+  sim_bcs?: unknown[];
+  mass_properties?: {
+    total_mass?: number;
+    center_of_gravity?: { x: number; y: number; z: number };
+    [key: string]: unknown;
+  };
+}
+
+interface ModelFileInfo {
+  name: string;
+  stem: string;
+  type: string;
+  description?: string;
+  size_kb: number;
+  has_cache: boolean;
+}
+
+interface ModelFilesResponse {
+  files: {
+    sim: ModelFileInfo[];
+    afm: ModelFileInfo[];
+    fem: ModelFileInfo[];
+    idealized: ModelFileInfo[];
+    prt: ModelFileInfo[];
+  };
+  all_files: ModelFileInfo[];
+}
+
+export function FloatingIntrospectionPanel({ onClose }: FloatingIntrospectionPanelProps) {
+  const panel = useIntrospectionPanel();
+  const { 
+    minimizePanel, 
+    updateIntrospectionResult, 
+    setIntrospectionLoading, 
+    setIntrospectionError,
+    setIntrospectionFile,
+  } = usePanelStore();
+  const { addNode } = useSpecStore();
+
+  // Local UI state
+  const [expandedSections, setExpandedSections] = useState<Set<string>>(
+    new Set(['expressions', 'extractors', 'file_deps', 'fea_results', 'fem_mesh', 'sim_solutions', 'sim_bcs'])
+  );
+  const [searchTerm, setSearchTerm] = useState('');
+  const [modelFiles, setModelFiles] = useState<ModelFilesResponse | null>(null);
+  const [isLoadingFiles, setIsLoadingFiles] = useState(false);
+
+  const data = panel.data;
+  const result = data?.result as IntrospectionResult | undefined;
+  const isLoading = data?.isLoading || false;
+  const error = data?.error as string | null;
+
+  // Fetch available files when studyId changes
+  const fetchAvailableFiles = useCallback(async () => {
+    if (!data?.studyId) return;
+    
+    setIsLoadingFiles(true);
+    try {
+      const res = await fetch(`/api/optimization/studies/${data.studyId}/nx/parts`);
+      if (res.ok) {
+        const filesData = await res.json();
+        setModelFiles(filesData);
+      }
+    } catch (e) {
+      console.error('Failed to fetch model files:', e);
+    } finally {
+      setIsLoadingFiles(false);
+    }
+  }, [data?.studyId]);
+
+  // Run introspection
+  const runIntrospection = useCallback(async (fileName?: string) => {
+    if (!data?.filePath && !data?.studyId) return;
+
+    setIntrospectionLoading(true);
+    setIntrospectionError(null);
+    
+    try {
+      let res;
+      
+      if (data?.studyId) {
+        const endpoint = fileName 
+          ? `/api/optimization/studies/${data.studyId}/nx/introspect/${encodeURIComponent(fileName)}`
+          : `/api/optimization/studies/${data.studyId}/nx/introspect`;
+        res = await fetch(endpoint);
+      } else {
+        res = await fetch('/api/nx/introspect', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ file_path: data?.filePath }),
+        });
+      }
+      
+      if (!res.ok) {
+        const errData = await res.json().catch(() => ({}));
+        throw new Error(errData.detail || 'Introspection failed');
+      }
+      
+      const responseData = await res.json();
+      updateIntrospectionResult(responseData.introspection || responseData);
+    } catch (e) {
+      const msg = e instanceof Error ? e.message : 'Failed to introspect model';
+      setIntrospectionError(msg);
+      console.error('Introspection error:', e);
+    }
+  }, [data?.filePath, data?.studyId, setIntrospectionLoading, setIntrospectionError, updateIntrospectionResult]);
+
+  // Fetch files list on mount
+  useEffect(() => {
+    fetchAvailableFiles();
+  }, [fetchAvailableFiles]);
+
+  // Run introspection when panel opens or selected file changes
+  useEffect(() => {
+    if (panel.open && data && !result && !isLoading) {
+      runIntrospection(data.selectedFile);
+    }
+  }, [panel.open, data?.selectedFile]); // eslint-disable-line react-hooks/exhaustive-deps
+
+  const handleFileChange = (e: React.ChangeEvent<HTMLSelectElement>) => {
+    const newFile = e.target.value;
+    setIntrospectionFile(newFile);
+    runIntrospection(newFile);
+  };
+
+  const toggleSection = (section: string) => {
+    setExpandedSections((prev) => {
+      const next = new Set(prev);
+      if (next.has(section)) next.delete(section);
+      else next.add(section);
+      return next;
+    });
+  };
+
+  // Handle both array format (old) and object format (new API)
+  const allExpressions: Expression[] = useMemo(() => {
+    if (!result?.expressions) return [];
+    
+    if (Array.isArray(result.expressions)) {
+      return result.expressions as Expression[];
+    }
+    
+    const exprObj = result.expressions as ExpressionsResult;
+    return [...(exprObj.user || []), ...(exprObj.internal || [])];
+  }, [result?.expressions]);
+
+  const filteredExpressions = allExpressions.filter((e) =>
+    e.name.toLowerCase().includes(searchTerm.toLowerCase())
+  );
+
+  const addExpressionAsDesignVar = (expr: Expression) => {
+    const minValue = expr.min ?? expr.value * 0.5;
+    const maxValue = expr.max ?? expr.value * 1.5;
+    
+    addNode('designVar', {
+      name: expr.name,
+      expression_name: expr.name,
+      type: 'continuous',
+      bounds: { min: minValue, max: maxValue },
+      baseline: expr.value,
+      units: expr.unit || expr.units,
+      enabled: true,
+    });
+  };
+
+  if (!panel.open) return null;
+
+  // Minimized view
+  if (panel.minimized) {
+    return (
+      <div 
+        className="bg-dark-850 border border-dark-700 rounded-lg shadow-xl flex items-center gap-2 px-3 py-2 cursor-pointer hover:bg-dark-800 transition-colors"
+        onClick={() => minimizePanel('introspection')}
+      >
+        <Search size={16} className="text-primary-400" />
+        <span className="text-sm text-white font-medium">
+          Model Introspection
+          {data?.selectedFile && <span className="text-dark-400 ml-1">({data.selectedFile})</span>}
+        </span>
+        <Maximize2 size={14} className="text-dark-400" />
+      </div>
+    );
+  }
+
+  return (
+    <div className="bg-dark-850 border border-dark-700 rounded-xl w-80 max-h-[70vh] flex flex-col shadow-xl">
+      {/* Header */}
+      <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700">
+        <div className="flex items-center gap-2">
+          <Search size={16} className="text-primary-400" />
+          <span className="font-medium text-white text-sm">
+            Model Introspection
+            {data?.selectedFile && <span className="text-primary-400 ml-1">({data.selectedFile})</span>}
+          </span>
+        </div>
+        <div className="flex items-center gap-1">
+          <button
+            onClick={() => runIntrospection(data?.selectedFile)}
+            disabled={isLoading}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+            title="Refresh"
+          >
+            <RefreshCw size={14} className={isLoading ? 'animate-spin' : ''} />
+          </button>
+          <button
+            onClick={() => minimizePanel('introspection')}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+            title="Minimize"
+          >
+            <Minimize2 size={14} />
+          </button>
+          <button
+            onClick={onClose}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+          >
+            <X size={14} />
+          </button>
+        </div>
+      </div>
+
+      {/* File Selector + Search */}
+      <div className="px-4 py-2 border-b border-dark-700 space-y-2">
+        {data?.studyId && modelFiles && modelFiles.all_files.length > 0 && (
+          <div className="flex items-center gap-2">
+            <label className="text-xs text-dark-400 whitespace-nowrap">File:</label>
+            <select
+              value={data?.selectedFile || ''}
+              onChange={handleFileChange}
+              disabled={isLoading || isLoadingFiles}
+              className="flex-1 px-2 py-1.5 bg-dark-800 border border-dark-600 rounded-lg
+                         text-sm text-white focus:outline-none focus:border-primary-500
+                         disabled:opacity-50"
+            >
+              <option value="">Default (Assembly)</option>
+              
+              {modelFiles.files.sim.length > 0 && (
+                <optgroup label="Simulation (.sim)">
+                  {modelFiles.files.sim.map(f => (
+                    <option key={f.name} value={f.name}>
+                      {f.stem} ({f.size_kb > 1000 ? `${(f.size_kb/1024).toFixed(1)}MB` : `${f.size_kb}KB`})
+                    </option>
+                  ))}
+                </optgroup>
+              )}
+              
+              {modelFiles.files.afm.length > 0 && (
+                <optgroup label="Assembly FEM (.afm)">
+                  {modelFiles.files.afm.map(f => (
+                    <option key={f.name} value={f.name}>
+                      {f.stem} ({f.size_kb > 1000 ? `${(f.size_kb/1024).toFixed(1)}MB` : `${f.size_kb}KB`})
+                    </option>
+                  ))}
+                </optgroup>
+              )}
+              
+              {modelFiles.files.fem.length > 0 && (
+                <optgroup label="FEM (.fem)">
+                  {modelFiles.files.fem.map(f => (
+                    <option key={f.name} value={f.name}>
+                      {f.stem} ({f.size_kb > 1000 ? `${(f.size_kb/1024).toFixed(1)}MB` : `${f.size_kb}KB`})
+                    </option>
+                  ))}
+                </optgroup>
+              )}
+              
+              {modelFiles.files.prt.length > 0 && (
+                <optgroup label="Geometry (.prt)">
+                  {modelFiles.files.prt.map(f => (
+                    <option key={f.name} value={f.name}>
+                      {f.stem} ({f.size_kb > 1000 ? `${(f.size_kb/1024).toFixed(1)}MB` : `${f.size_kb}KB`})
+                    </option>
+                  ))}
+                </optgroup>
+              )}
+              
+              {modelFiles.files.idealized.length > 0 && (
+                <optgroup label="Idealized (_i.prt)">
+                  {modelFiles.files.idealized.map(f => (
+                    <option key={f.name} value={f.name}>
+                      {f.stem} ({f.size_kb > 1000 ? `${(f.size_kb/1024).toFixed(1)}MB` : `${f.size_kb}KB`})
+                    </option>
+                  ))}
+                </optgroup>
+              )}
+            </select>
+            {isLoadingFiles && (
+              <RefreshCw size={12} className="animate-spin text-dark-400" />
+            )}
+          </div>
+        )}
+        
+        <input
+          type="text"
+          placeholder="Filter expressions..."
+          value={searchTerm}
+          onChange={(e) => setSearchTerm(e.target.value)}
+          className="w-full px-3 py-1.5 bg-dark-800 border border-dark-600 rounded-lg
+                     text-sm text-white placeholder-dark-500 focus:outline-none focus:border-primary-500"
+        />
+      </div>
+
+      {/* Content */}
+      <div className="flex-1 overflow-auto">
+        {isLoading ? (
+          <div className="flex items-center justify-center h-32 text-dark-500">
+            <RefreshCw size={20} className="animate-spin mr-2" />
+            Analyzing model...
+          </div>
+        ) : error ? (
+          <div className="p-4 text-red-400 text-sm">{error}</div>
+        ) : result ? (
+          <div className="p-2 space-y-2">
+            {/* Solver Type */}
+            {result.solver_type && (
+              <div className="p-2 bg-dark-800 rounded-lg">
+                <div className="flex items-center gap-2 text-sm">
+                  <Cpu size={14} className="text-violet-400" />
+                  <span className="text-dark-300">Solver:</span>
+                  <span className="text-white font-medium">{result.solver_type as string}</span>
+                </div>
+              </div>
+            )}
+
+            {/* Expressions Section */}
+            <div className="border border-dark-700 rounded-lg overflow-hidden">
+              <button
+                onClick={() => toggleSection('expressions')}
+                className="w-full flex items-center justify-between px-3 py-2 bg-dark-800 hover:bg-dark-750 transition-colors"
+              >
+                <div className="flex items-center gap-2">
+                  <SlidersHorizontal size={14} className="text-emerald-400" />
+                  <span className="text-sm font-medium text-white">
+                    Expressions ({filteredExpressions.length})
+                  </span>
+                </div>
+                {expandedSections.has('expressions') ? (
+                  <ChevronDown size={14} className="text-dark-400" />
+                ) : (
+                  <ChevronRight size={14} className="text-dark-400" />
+                )}
+              </button>
+
+              {expandedSections.has('expressions') && (
+                <div className="p-2 space-y-1 max-h-48 overflow-y-auto">
+                  {filteredExpressions.length === 0 ? (
+                    <p className="text-xs text-dark-500 text-center py-2">
+                      No expressions found
+                    </p>
+                  ) : (
+                    filteredExpressions.map((expr) => (
+                      <div
+                        key={expr.name}
+                        className="flex items-center justify-between p-2 bg-dark-850 rounded hover:bg-dark-750 group transition-colors"
+                      >
+                        <div className="flex-1 min-w-0">
+                          <p className="text-sm text-white truncate">{expr.name}</p>
+                          <p className="text-xs text-dark-500">
+                            {expr.value} {expr.units || expr.unit || ''}
+                            {expr.source === 'inferred' && (
+                              <span className="ml-1 text-amber-500">(inferred)</span>
+                            )}
+                          </p>
+                        </div>
+                        <button
+                          onClick={() => addExpressionAsDesignVar(expr)}
+                          className="p-1.5 text-dark-500 hover:text-primary-400 hover:bg-dark-700 rounded
+                                     opacity-0 group-hover:opacity-100 transition-all"
+                          title="Add as Design Variable"
+                        >
+                          <Plus size={14} />
+                        </button>
+                      </div>
+                    ))
+                  )}
+                </div>
+              )}
+            </div>
+
+            {/* Mass Properties Section */}
+            {result.mass_properties && (
+              <div className="border border-dark-700 rounded-lg overflow-hidden">
+                <button
+                  onClick={() => toggleSection('mass')}
+                  className="w-full flex items-center justify-between px-3 py-2 bg-dark-800 hover:bg-dark-750 transition-colors"
+                >
+                  <div className="flex items-center gap-2">
+                    <Scale size={14} className="text-blue-400" />
+                    <span className="text-sm font-medium text-white">Mass Properties</span>
+                  </div>
+                  {expandedSections.has('mass') ? (
+                    <ChevronDown size={14} className="text-dark-400" />
+                  ) : (
+                    <ChevronRight size={14} className="text-dark-400" />
+                  )}
+                </button>
+
+                {expandedSections.has('mass') && (
+                  <div className="p-2 space-y-1">
+                    {(result.mass_properties as Record<string, unknown>).mass_kg !== undefined && (
+                      <div className="flex justify-between p-2 bg-dark-850 rounded text-xs">
+                        <span className="text-dark-400">Mass</span>
+                        <span className="text-white font-mono">
+                          {((result.mass_properties as Record<string, unknown>).mass_kg as number).toFixed(4)} kg
+                        </span>
+                      </div>
+                    )}
+                  </div>
+                )}
+              </div>
+            )}
+
+            {/* More sections can be added here following the same pattern as the original IntrospectionPanel */}
+          </div>
+        ) : (
+          <div className="p-4 text-center text-dark-500 text-sm">
+            Click refresh to analyze the model
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
+export default FloatingIntrospectionPanel;

atomizer-dashboard/frontend/src/components/canvas/panels/NodeConfigPanelV2.tsx

@@ -17,8 +17,8 @@ import {
   useSelectedNodeId,
   useSelectedNode,
 } from '../../../hooks/useSpecStore';
+import { usePanelStore } from '../../../hooks/usePanelStore';
 import { FileBrowser } from './FileBrowser';
-import { IntrospectionPanel } from './IntrospectionPanel';
 import {
   DesignVariable,
   Extractor,
@@ -43,7 +43,6 @@ export function NodeConfigPanelV2({ onClose }: NodeConfigPanelV2Props) {
   const { updateNode, removeNode, clearSelection } = useSpecStore();
   
   const [showFileBrowser, setShowFileBrowser] = useState(false);
-  const [showIntrospection, setShowIntrospection] = useState(false);
   const [isUpdating, setIsUpdating] = useState(false);
   const [error, setError] = useState<string | null>(null);
 
@@ -249,15 +248,7 @@ export function NodeConfigPanelV2({ onClose }: NodeConfigPanelV2Props) {
         fileTypes={['.sim', '.prt', '.fem', '.afem']}
       />
 
-      {/* Introspection Panel */}
-      {showIntrospection && spec.model.sim?.path && (
-        <div className="fixed top-20 right-96 z-40">
-          <IntrospectionPanel
-            filePath={spec.model.sim.path}
-            onClose={() => setShowIntrospection(false)}
-          />
-        </div>
-      )}
+      {/* Introspection is now handled by FloatingIntrospectionPanel via usePanelStore */}
     </div>
   );
 }
@@ -271,7 +262,16 @@ interface SpecConfigProps {
 }
 
 function ModelNodeConfig({ spec }: SpecConfigProps) {
-  const [showIntrospection, setShowIntrospection] = useState(false);
+  const { setIntrospectionData, openPanel } = usePanelStore();
+  
+  const handleOpenIntrospection = () => {
+    // Set up introspection data and open the panel
+    setIntrospectionData({
+      filePath: spec.model.sim?.path || '',
+      studyId: useSpecStore.getState().studyId || undefined,
+    });
+    openPanel('introspection');
+  };
   
   return (
     <>
@@ -299,7 +299,7 @@ function ModelNodeConfig({ spec }: SpecConfigProps) {
 
       {spec.model.sim?.path && (
         <button
-          onClick={() => setShowIntrospection(true)}
+          onClick={handleOpenIntrospection}
           className="w-full flex items-center justify-center gap-2 px-3 py-2.5 bg-primary-500/20
                      hover:bg-primary-500/30 border border-primary-500/30 rounded-lg
                      text-primary-400 text-sm font-medium transition-colors"
@@ -308,32 +308,113 @@ function ModelNodeConfig({ spec }: SpecConfigProps) {
           Introspect Model
         </button>
       )}
-
-      {showIntrospection && spec.model.sim?.path && (
-        <div className="fixed top-20 right-96 z-40">
-          <IntrospectionPanel
-            filePath={spec.model.sim.path}
-            onClose={() => setShowIntrospection(false)}
-          />
-        </div>
-      )}
+      
+      {/* Note: IntrospectionPanel is now rendered by PanelContainer, not here */}
     </>
   );
 }
 
 function SolverNodeConfig({ spec }: SpecConfigProps) {
+  const { patchSpec } = useSpecStore();
+  const [isUpdating, setIsUpdating] = useState(false);
+  
+  const engine = spec.model.sim?.engine || 'nxnastran';
+  const solutionType = spec.model.sim?.solution_type || 'SOL101';
+  const scriptPath = spec.model.sim?.script_path || '';
+  const isPython = engine === 'python';
+  
+  const handleEngineChange = async (newEngine: string) => {
+    setIsUpdating(true);
+    try {
+      await patchSpec('model.sim.engine', newEngine);
+    } catch (err) {
+      console.error('Failed to update engine:', err);
+    } finally {
+      setIsUpdating(false);
+    }
+  };
+  
+  const handleSolutionTypeChange = async (newType: string) => {
+    setIsUpdating(true);
+    try {
+      await patchSpec('model.sim.solution_type', newType);
+    } catch (err) {
+      console.error('Failed to update solution type:', err);
+    } finally {
+      setIsUpdating(false);
+    }
+  };
+  
+  const handleScriptPathChange = async (newPath: string) => {
+    setIsUpdating(true);
+    try {
+      await patchSpec('model.sim.script_path', newPath);
+    } catch (err) {
+      console.error('Failed to update script path:', err);
+    } finally {
+      setIsUpdating(false);
+    }
+  };
+
   return (
-    <div>
-      <label className={labelClass}>Solution Type</label>
-      <input
-        type="text"
-        value={spec.model.sim?.solution_type || 'Not configured'}
-        readOnly
-        className={`${inputClass} bg-dark-900 cursor-not-allowed`}
-        title="Solver type is determined by the model file."
-      />
-      <p className="text-xs text-dark-500 mt-1">Detected from model file.</p>
-    </div>
+    <>
+      {isUpdating && (
+        <div className="text-xs text-primary-400 animate-pulse">Updating...</div>
+      )}
+      
+      <div>
+        <label className={labelClass}>Solver Engine</label>
+        <select
+          value={engine}
+          onChange={(e) => handleEngineChange(e.target.value)}
+          className={selectClass}
+        >
+          <option value="nxnastran">NX Nastran (built-in)</option>
+          <option value="mscnastran">MSC Nastran (external)</option>
+          <option value="python">Python Script</option>
+          <option value="abaqus" disabled>Abaqus (coming soon)</option>
+          <option value="ansys" disabled>ANSYS (coming soon)</option>
+        </select>
+        <p className="text-xs text-dark-500 mt-1">
+          {isPython ? 'Run custom Python analysis script' : 'Select FEA solver software'}
+        </p>
+      </div>
+      
+      {!isPython && (
+        <div>
+          <label className={labelClass}>Solution Type</label>
+          <select
+            value={solutionType}
+            onChange={(e) => handleSolutionTypeChange(e.target.value)}
+            className={selectClass}
+          >
+            <option value="SOL101">SOL101 - Linear Statics</option>
+            <option value="SOL103">SOL103 - Normal Modes</option>
+            <option value="SOL105">SOL105 - Buckling</option>
+            <option value="SOL106">SOL106 - Nonlinear Statics</option>
+            <option value="SOL111">SOL111 - Modal Frequency Response</option>
+            <option value="SOL112">SOL112 - Modal Transient Response</option>
+            <option value="SOL200">SOL200 - Design Optimization</option>
+          </select>
+        </div>
+      )}
+      
+      {isPython && (
+        <div>
+          <label className={labelClass}>Script Path</label>
+          <input
+            type="text"
+            value={scriptPath}
+            onChange={(e) => handleScriptPathChange(e.target.value)}
+            placeholder="path/to/solver_script.py"
+            className={`${inputClass} font-mono text-sm`}
+          />
+          <p className="text-xs text-dark-500 mt-1">
+            Python script must define solve(params) function
+          </p>
+        </div>
+      )}
+    </>
   );
 }
 
@@ -694,38 +775,21 @@ function ExtractorNodeConfig({ node, onChange }: ExtractorNodeConfigProps) {
       {showCodeEditor && (
         <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/60 backdrop-blur-sm">
           <div className="w-[900px] h-[700px] bg-dark-850 rounded-xl overflow-hidden shadow-2xl border border-dark-600 flex flex-col">
-            {/* Modal Header */}
-            <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700 bg-dark-900">
-              <div className="flex items-center gap-3">
-                <FileCode size={18} className="text-violet-400" />
-                <span className="font-medium text-white">Custom Extractor: {node.name}</span>
-                <span className="text-xs text-dark-500 bg-dark-800 px-2 py-0.5 rounded">.py</span>
-              </div>
-              <button
-                onClick={() => setShowCodeEditor(false)}
-                className="p-1.5 rounded hover:bg-dark-700 text-dark-400 hover:text-white transition-colors"
-              >
-                <X size={18} />
-              </button>
-            </div>
-            
-            {/* Code Editor */}
-            <div className="flex-1">
-              <CodeEditorPanel
-                initialCode={currentCode}
-                extractorName={node.name}
-                outputs={node.outputs?.map(o => o.name) || []}
-                onChange={handleCodeChange}
-                onRequestGeneration={handleRequestGeneration}
-                onRequestStreamingGeneration={handleStreamingGeneration}
-                onRun={handleValidateCode}
-                onTest={handleTestCode}
-                onClose={() => setShowCodeEditor(false)}
-                showHeader={false}
-                height="100%"
-                studyId={studyId || undefined}
-              />
-            </div>
+            {/* Code Editor with built-in header containing toolbar buttons */}
+            <CodeEditorPanel
+              initialCode={currentCode}
+              extractorName={`Custom Extractor: ${node.name}`}
+              outputs={node.outputs?.map(o => o.name) || []}
+              onChange={handleCodeChange}
+              onRequestGeneration={handleRequestGeneration}
+              onRequestStreamingGeneration={handleStreamingGeneration}
+              onRun={handleValidateCode}
+              onTest={handleTestCode}
+              onClose={() => setShowCodeEditor(false)}
+              showHeader={true}
+              height="100%"
+              studyId={studyId || undefined}
+            />
           </div>
         </div>
       )}
@@ -756,6 +820,34 @@ interface ObjectiveNodeConfigProps {
 }
 
 function ObjectiveNodeConfig({ node, onChange }: ObjectiveNodeConfigProps) {
+  const spec = useSpec();
+  const extractors = spec?.extractors || [];
+
+  const currentExtractorId = node.source?.extractor_id || '__UNSET__';
+  const currentOutputName = node.source?.output_name || '__UNSET__';
+
+  const selectedExtractor = extractors.find((e) => e.id === currentExtractorId);
+  const outputOptions = selectedExtractor?.outputs?.map((o) => o.name) || [];
+
+  const handleExtractorChange = (extractorId: string) => {
+    // Reset output_name to a sensible default when extractor changes
+    const ext = extractors.find((e) => e.id === extractorId);
+    const outs = ext?.outputs?.map((o) => o.name) || [];
+    const preferred = outs.includes('value') ? 'value' : outs[0] || '__UNSET__';
+
+    onChange('source', {
+      extractor_id: extractorId,
+      output_name: preferred,
+    });
+  };
+
+  const handleOutputChange = (outputName: string) => {
+    onChange('source', {
+      extractor_id: currentExtractorId,
+      output_name: outputName,
+    });
+  };
+
   return (
     <>
       <div>
@@ -767,6 +859,45 @@ function ObjectiveNodeConfig({ node, onChange }: ObjectiveNodeConfigProps) {
           className={inputClass}
         />
       </div>
+
+      <div>
+        <label className={labelClass}>Source Extractor</label>
+        <select
+          value={currentExtractorId}
+          onChange={(e) => handleExtractorChange(e.target.value)}
+          className={selectClass}
+        >
+          <option value="__UNSET__">(not connected)</option>
+          {extractors.map((ext) => (
+            <option key={ext.id} value={ext.id}>
+              {ext.id} — {ext.name}
+            </option>
+          ))}
+        </select>
+      </div>
+
+      <div>
+        <label className={labelClass}>Source Output</label>
+        <select
+          value={currentOutputName}
+          onChange={(e) => handleOutputChange(e.target.value)}
+          className={selectClass}
+          disabled={currentExtractorId === '__UNSET__'}
+        >
+          {currentExtractorId === '__UNSET__' ? (
+            <option value="__UNSET__">(select an extractor)</option>
+          ) : (
+            outputOptions.map((name) => (
+              <option key={name} value={name}>
+                {name}
+              </option>
+            ))
+          )}
+        </select>
+        <p className="text-xs text-dark-500 mt-1">
+          This drives execution. Canvas wires are just a visual check.
+        </p>
+      </div>
       
       <div>
         <label className={labelClass}>Direction</label>
@@ -813,6 +944,33 @@ interface ConstraintNodeConfigProps {
 }
 
 function ConstraintNodeConfig({ node, onChange }: ConstraintNodeConfigProps) {
+  const spec = useSpec();
+  const extractors = spec?.extractors || [];
+
+  const currentExtractorId = node.source?.extractor_id || '__UNSET__';
+  const currentOutputName = node.source?.output_name || '__UNSET__';
+
+  const selectedExtractor = extractors.find((e) => e.id === currentExtractorId);
+  const outputOptions = selectedExtractor?.outputs?.map((o) => o.name) || [];
+
+  const handleExtractorChange = (extractorId: string) => {
+    const ext = extractors.find((e) => e.id === extractorId);
+    const outs = ext?.outputs?.map((o) => o.name) || [];
+    const preferred = outs.includes('value') ? 'value' : outs[0] || '__UNSET__';
+
+    onChange('source', {
+      extractor_id: extractorId,
+      output_name: preferred,
+    });
+  };
+
+  const handleOutputChange = (outputName: string) => {
+    onChange('source', {
+      extractor_id: currentExtractorId,
+      output_name: outputName,
+    });
+  };
+
   return (
     <>
       <div>
@@ -824,6 +982,45 @@ function ConstraintNodeConfig({ node, onChange }: ConstraintNodeConfigProps) {
           className={inputClass}
         />
       </div>
+
+      <div>
+        <label className={labelClass}>Source Extractor</label>
+        <select
+          value={currentExtractorId}
+          onChange={(e) => handleExtractorChange(e.target.value)}
+          className={selectClass}
+        >
+          <option value="__UNSET__">(not connected)</option>
+          {extractors.map((ext) => (
+            <option key={ext.id} value={ext.id}>
+              {ext.id} — {ext.name}
+            </option>
+          ))}
+        </select>
+      </div>
+
+      <div>
+        <label className={labelClass}>Source Output</label>
+        <select
+          value={currentOutputName}
+          onChange={(e) => handleOutputChange(e.target.value)}
+          className={selectClass}
+          disabled={currentExtractorId === '__UNSET__'}
+        >
+          {currentExtractorId === '__UNSET__' ? (
+            <option value="__UNSET__">(select an extractor)</option>
+          ) : (
+            outputOptions.map((name) => (
+              <option key={name} value={name}>
+                {name}
+              </option>
+            ))
+          )}
+        </select>
+        <p className="text-xs text-dark-500 mt-1">
+          This drives execution. Canvas wires are just a visual check.
+        </p>
+      </div>
       
       <div className="grid grid-cols-2 gap-2">
         <div>
@@ -833,24 +1030,37 @@ function ConstraintNodeConfig({ node, onChange }: ConstraintNodeConfigProps) {
             onChange={(e) => onChange('type', e.target.value)}
             className={selectClass}
           >
-            <option value="less_than">&lt; Less than</option>
-            <option value="less_equal">&lt;= Less or equal</option>
-            <option value="greater_than">&gt; Greater than</option>
-            <option value="greater_equal">&gt;= Greater or equal</option>
-            <option value="equal">= Equal</option>
+            <option value="hard">Hard</option>
+            <option value="soft">Soft</option>
           </select>
+          <p className="text-xs text-dark-500 mt-1">Spec type (hard/soft). Operator is set below.</p>
         </div>
         <div>
-          <label className={labelClass}>Threshold</label>
-          <input
-            type="number"
-            value={node.threshold}
-            onChange={(e) => onChange('threshold', parseFloat(e.target.value))}
-            className={inputClass}
-          />
+          <label className={labelClass}>Operator</label>
+          <select
+            value={node.operator}
+            onChange={(e) => onChange('operator', e.target.value)}
+            className={selectClass}
+          >
+            <option value="<=">&lt;=</option>
+            <option value="<">&lt;</option>
+            <option value=">=">&gt;=</option>
+            <option value=">">&gt;</option>
+            <option value="==">==</option>
+          </select>
         </div>
       </div>
 
+      <div>
+        <label className={labelClass}>Threshold</label>
+        <input
+          type="number"
+          value={node.threshold}
+          onChange={(e) => onChange('threshold', parseFloat(e.target.value))}
+          className={inputClass}
+        />
+      </div>
+
     </>
   );
 }

atomizer-dashboard/frontend/src/components/canvas/panels/PanelContainer.tsx

@@ -0,0 +1,207 @@
+/**
+ * PanelContainer - Orchestrates all floating panels in the canvas view
+ * 
+ * This component renders floating panels (Introspection, Validation, Error, Results)
+ * in a portal, positioned absolutely within the canvas area.
+ * 
+ * Features:
+ * - Draggable panels
+ * - Z-index management (click to bring to front)
+ * - Keyboard shortcuts (Escape to close all)
+ * - Position persistence via usePanelStore
+ */
+
+import { useState, useCallback, useEffect, useRef } from 'react';
+import { createPortal } from 'react-dom';
+import {
+  usePanelStore,
+  useIntrospectionPanel,
+  useValidationPanel,
+  useErrorPanel,
+  useResultsPanel,
+  PanelPosition,
+} from '../../../hooks/usePanelStore';
+import { FloatingIntrospectionPanel } from './FloatingIntrospectionPanel';
+import { FloatingValidationPanel } from './ValidationPanel';
+import { ErrorPanel } from './ErrorPanel';
+import { ResultsPanel } from './ResultsPanel';
+
+interface PanelContainerProps {
+  /** Container element to render panels into (defaults to document.body) */
+  container?: HTMLElement;
+  /** Callback when retry is requested from error panel */
+  onRetry?: (trial?: number) => void;
+  /** Callback when skip trial is requested */
+  onSkipTrial?: (trial: number) => void;
+}
+
+type PanelName = 'introspection' | 'validation' | 'error' | 'results';
+
+export function PanelContainer({ container, onRetry, onSkipTrial }: PanelContainerProps) {
+  const { closePanel, setPanelPosition, closeAllPanels } = usePanelStore();
+  
+  const introspectionPanel = useIntrospectionPanel();
+  const validationPanel = useValidationPanel();
+  const errorPanel = useErrorPanel();
+  const resultsPanel = useResultsPanel();
+
+  // Track which panel is on top (for z-index)
+  const [topPanel, setTopPanel] = useState<PanelName | null>(null);
+  
+  // Dragging state
+  const [dragging, setDragging] = useState<{ panel: PanelName; offset: { x: number; y: number } } | null>(null);
+  const dragRef = useRef<{ panel: PanelName; offset: { x: number; y: number } } | null>(null);
+
+  // Escape key to close all panels
+  useEffect(() => {
+    const handleKeyDown = (e: KeyboardEvent) => {
+      if (e.key === 'Escape') {
+        closeAllPanels();
+      }
+    };
+    window.addEventListener('keydown', handleKeyDown);
+    return () => window.removeEventListener('keydown', handleKeyDown);
+  }, [closeAllPanels]);
+
+  // Mouse move handler for dragging
+  useEffect(() => {
+    const handleMouseMove = (e: MouseEvent) => {
+      if (!dragRef.current) return;
+      
+      const { panel, offset } = dragRef.current;
+      const newPosition: PanelPosition = {
+        x: e.clientX - offset.x,
+        y: e.clientY - offset.y,
+      };
+      
+      // Clamp to viewport
+      newPosition.x = Math.max(0, Math.min(window.innerWidth - 100, newPosition.x));
+      newPosition.y = Math.max(0, Math.min(window.innerHeight - 50, newPosition.y));
+      
+      setPanelPosition(panel, newPosition);
+    };
+
+    const handleMouseUp = () => {
+      dragRef.current = null;
+      setDragging(null);
+    };
+
+    if (dragging) {
+      window.addEventListener('mousemove', handleMouseMove);
+      window.addEventListener('mouseup', handleMouseUp);
+    }
+
+    return () => {
+      window.removeEventListener('mousemove', handleMouseMove);
+      window.removeEventListener('mouseup', handleMouseUp);
+    };
+  }, [dragging, setPanelPosition]);
+
+  // Start dragging a panel
+  const handleDragStart = useCallback((panel: PanelName, e: React.MouseEvent, position: PanelPosition) => {
+    const offset = {
+      x: e.clientX - position.x,
+      y: e.clientY - position.y,
+    };
+    dragRef.current = { panel, offset };
+    setDragging({ panel, offset });
+    setTopPanel(panel);
+  }, []);
+
+  // Click to bring panel to front
+  const handlePanelClick = useCallback((panel: PanelName) => {
+    setTopPanel(panel);
+  }, []);
+
+  // Get z-index for a panel
+  const getZIndex = (panel: PanelName) => {
+    const baseZ = 100;
+    if (panel === topPanel) return baseZ + 10;
+    return baseZ;
+  };
+
+  // Render a draggable wrapper
+  const renderDraggable = (
+    panel: PanelName,
+    position: PanelPosition,
+    isOpen: boolean,
+    children: React.ReactNode
+  ) => {
+    if (!isOpen) return null;
+
+    return (
+      <div
+        key={panel}
+        className="fixed select-none"
+        style={{
+          left: position.x,
+          top: position.y,
+          zIndex: getZIndex(panel),
+          cursor: dragging?.panel === panel ? 'grabbing' : 'default',
+        }}
+        onClick={() => handlePanelClick(panel)}
+      >
+        {/* Drag handle - the header area */}
+        <div
+          className="absolute top-0 left-0 right-0 h-12 cursor-grab active:cursor-grabbing"
+          onMouseDown={(e) => handleDragStart(panel, e, position)}
+          style={{ zIndex: 1 }}
+        />
+        {/* Panel content */}
+        <div className="relative" style={{ zIndex: 0 }}>
+          {children}
+        </div>
+      </div>
+    );
+  };
+
+  // Determine what to render
+  const panels = (
+    <>
+      {/* Introspection Panel */}
+      {renderDraggable(
+        'introspection',
+        introspectionPanel.position || { x: 100, y: 100 },
+        introspectionPanel.open,
+        <FloatingIntrospectionPanel onClose={() => closePanel('introspection')} />
+      )}
+
+      {/* Validation Panel */}
+      {renderDraggable(
+        'validation',
+        validationPanel.position || { x: 150, y: 150 },
+        validationPanel.open,
+        <FloatingValidationPanel onClose={() => closePanel('validation')} />
+      )}
+
+      {/* Error Panel */}
+      {renderDraggable(
+        'error',
+        errorPanel.position || { x: 200, y: 100 },
+        errorPanel.open,
+        <ErrorPanel 
+          onClose={() => closePanel('error')} 
+          onRetry={onRetry}
+          onSkipTrial={onSkipTrial}
+        />
+      )}
+
+      {/* Results Panel */}
+      {renderDraggable(
+        'results',
+        resultsPanel.position || { x: 250, y: 150 },
+        resultsPanel.open,
+        <ResultsPanel onClose={() => closePanel('results')} />
+      )}
+    </>
+  );
+
+  // Use portal if container specified, otherwise render in place
+  if (container) {
+    return createPortal(panels, container);
+  }
+
+  return panels;
+}
+
+export default PanelContainer;

atomizer-dashboard/frontend/src/components/canvas/panels/ResultsPanel.tsx

@@ -0,0 +1,179 @@
+/**
+ * ResultsPanel - Shows detailed trial results
+ * 
+ * Displays the parameters, objectives, and constraints for a specific trial.
+ * Can be opened by clicking on result badges on nodes.
+ */
+
+import {
+  X,
+  Minimize2,
+  Maximize2,
+  CheckCircle,
+  XCircle,
+  Trophy,
+  SlidersHorizontal,
+  Target,
+  AlertTriangle,
+  Clock,
+} from 'lucide-react';
+import { useResultsPanel, usePanelStore } from '../../../hooks/usePanelStore';
+
+interface ResultsPanelProps {
+  onClose: () => void;
+}
+
+export function ResultsPanel({ onClose }: ResultsPanelProps) {
+  const panel = useResultsPanel();
+  const { minimizePanel } = usePanelStore();
+  const data = panel.data;
+
+  if (!panel.open || !data) return null;
+
+  const timestamp = new Date(data.timestamp).toLocaleTimeString();
+
+  // Minimized view
+  if (panel.minimized) {
+    return (
+      <div 
+        className="bg-dark-850 border border-dark-700 rounded-lg shadow-xl flex items-center gap-2 px-3 py-2 cursor-pointer hover:bg-dark-800 transition-colors"
+        onClick={() => minimizePanel('results')}
+      >
+        <Trophy size={16} className={data.isBest ? 'text-amber-400' : 'text-dark-400'} />
+        <span className="text-sm text-white font-medium">
+          Trial #{data.trialNumber}
+        </span>
+        <Maximize2 size={14} className="text-dark-400" />
+      </div>
+    );
+  }
+
+  return (
+    <div className="bg-dark-850 border border-dark-700 rounded-xl w-80 max-h-[500px] flex flex-col shadow-xl">
+      {/* Header */}
+      <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700">
+        <div className="flex items-center gap-2">
+          <Trophy size={18} className={data.isBest ? 'text-amber-400' : 'text-dark-400'} />
+          <span className="font-medium text-white">
+            Trial #{data.trialNumber}
+          </span>
+          {data.isBest && (
+            <span className="px-1.5 py-0.5 text-xs bg-amber-500/20 text-amber-400 rounded">
+              Best
+            </span>
+          )}
+        </div>
+        <div className="flex items-center gap-1">
+          <button
+            onClick={() => minimizePanel('results')}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+            title="Minimize"
+          >
+            <Minimize2 size={14} />
+          </button>
+          <button
+            onClick={onClose}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+          >
+            <X size={14} />
+          </button>
+        </div>
+      </div>
+
+      {/* Content */}
+      <div className="flex-1 overflow-y-auto p-3 space-y-4">
+        {/* Status */}
+        <div className="flex items-center gap-3">
+          {data.isFeasible ? (
+            <div className="flex items-center gap-1.5 text-green-400">
+              <CheckCircle size={16} />
+              <span className="text-sm font-medium">Feasible</span>
+            </div>
+          ) : (
+            <div className="flex items-center gap-1.5 text-red-400">
+              <XCircle size={16} />
+              <span className="text-sm font-medium">Infeasible</span>
+            </div>
+          )}
+          <div className="flex items-center gap-1.5 text-dark-400 ml-auto">
+            <Clock size={14} />
+            <span className="text-xs">{timestamp}</span>
+          </div>
+        </div>
+
+        {/* Parameters */}
+        <div>
+          <h4 className="text-xs font-medium text-dark-400 uppercase tracking-wide mb-2 flex items-center gap-1.5">
+            <SlidersHorizontal size={12} />
+            Parameters
+          </h4>
+          <div className="space-y-1">
+            {Object.entries(data.params).map(([name, value]) => (
+              <div key={name} className="flex justify-between p-2 bg-dark-800 rounded text-sm">
+                <span className="text-dark-300">{name}</span>
+                <span className="text-white font-mono">{formatValue(value)}</span>
+              </div>
+            ))}
+          </div>
+        </div>
+
+        {/* Objectives */}
+        <div>
+          <h4 className="text-xs font-medium text-dark-400 uppercase tracking-wide mb-2 flex items-center gap-1.5">
+            <Target size={12} />
+            Objectives
+          </h4>
+          <div className="space-y-1">
+            {Object.entries(data.objectives).map(([name, value]) => (
+              <div key={name} className="flex justify-between p-2 bg-dark-800 rounded text-sm">
+                <span className="text-dark-300">{name}</span>
+                <span className="text-primary-400 font-mono">{formatValue(value)}</span>
+              </div>
+            ))}
+          </div>
+        </div>
+
+        {/* Constraints (if any) */}
+        {data.constraints && Object.keys(data.constraints).length > 0 && (
+          <div>
+            <h4 className="text-xs font-medium text-dark-400 uppercase tracking-wide mb-2 flex items-center gap-1.5">
+              <AlertTriangle size={12} />
+              Constraints
+            </h4>
+            <div className="space-y-1">
+              {Object.entries(data.constraints).map(([name, constraint]) => (
+                <div 
+                  key={name} 
+                  className={`flex justify-between p-2 rounded text-sm ${
+                    constraint.feasible ? 'bg-dark-800' : 'bg-red-500/10 border border-red-500/20'
+                  }`}
+                >
+                  <span className="text-dark-300 flex items-center gap-1.5">
+                    {constraint.feasible ? (
+                      <CheckCircle size={12} className="text-green-400" />
+                    ) : (
+                      <XCircle size={12} className="text-red-400" />
+                    )}
+                    {name}
+                  </span>
+                  <span className={`font-mono ${constraint.feasible ? 'text-white' : 'text-red-400'}`}>
+                    {formatValue(constraint.value)}
+                  </span>
+                </div>
+              ))}
+            </div>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
+function formatValue(value: number): string {
+  if (Math.abs(value) < 0.001 || Math.abs(value) >= 10000) {
+    return value.toExponential(3);
+  }
+  return value.toFixed(4).replace(/\.?0+$/, '');
+}
+
+export default ResultsPanel;

atomizer-dashboard/frontend/src/components/canvas/panels/ValidationPanel.tsx

@@ -1,10 +1,41 @@
+/**
+ * ValidationPanel - Displays spec validation errors and warnings
+ * 
+ * Shows a list of validation issues that need to be fixed before
+ * running an optimization. Supports auto-navigation to problematic nodes.
+ * 
+ * Can be used in two modes:
+ * 1. Legacy mode: Pass validation prop directly (for backward compatibility)
+ * 2. Store mode: Uses usePanelStore for persistent state
+ */
+
+import { useMemo } from 'react';
+import {
+  X,
+  AlertCircle,
+  AlertTriangle,
+  CheckCircle,
+  ChevronRight,
+  Minimize2,
+  Maximize2,
+} from 'lucide-react';
+import { useValidationPanel, usePanelStore, ValidationError as StoreValidationError } from '../../../hooks/usePanelStore';
+import { useSpecStore } from '../../../hooks/useSpecStore';
 import { ValidationResult } from '../../../lib/canvas/validation';
 
-interface ValidationPanelProps {
+// ============================================================================
+// Legacy Props Interface (for backward compatibility)
+// ============================================================================
+
+interface LegacyValidationPanelProps {
   validation: ValidationResult;
 }
 
-export function ValidationPanel({ validation }: ValidationPanelProps) {
+/**
+ * Legacy ValidationPanel - Inline display for canvas overlay
+ * Kept for backward compatibility with AtomizerCanvas
+ */
+export function ValidationPanel({ validation }: LegacyValidationPanelProps) {
   return (
     <div className="absolute top-4 left-1/2 transform -translate-x-1/2 max-w-md w-full z-10">
       {validation.errors.length > 0 && (
@@ -30,3 +61,199 @@ export function ValidationPanel({ validation }: ValidationPanelProps) {
     </div>
   );
 }
+
+// ============================================================================
+// New Floating Panel (uses store)
+// ============================================================================
+
+interface FloatingValidationPanelProps {
+  onClose: () => void;
+}
+
+export function FloatingValidationPanel({ onClose }: FloatingValidationPanelProps) {
+  const panel = useValidationPanel();
+  const { minimizePanel } = usePanelStore();
+  const { selectNode } = useSpecStore();
+
+  const { errors, warnings, valid } = useMemo(() => {
+    if (!panel.data) {
+      return { errors: [], warnings: [], valid: true };
+    }
+    return {
+      errors: panel.data.errors || [],
+      warnings: panel.data.warnings || [],
+      valid: panel.data.valid,
+    };
+  }, [panel.data]);
+
+  const handleNavigateToNode = (nodeId?: string) => {
+    if (nodeId) {
+      selectNode(nodeId);
+    }
+  };
+
+  if (!panel.open) return null;
+
+  // Minimized view
+  if (panel.minimized) {
+    return (
+      <div 
+        className="bg-dark-850 border border-dark-700 rounded-lg shadow-xl flex items-center gap-2 px-3 py-2 cursor-pointer hover:bg-dark-800 transition-colors"
+        onClick={() => minimizePanel('validation')}
+      >
+        {valid ? (
+          <CheckCircle size={16} className="text-green-400" />
+        ) : (
+          <AlertCircle size={16} className="text-red-400" />
+        )}
+        <span className="text-sm text-white font-medium">
+          Validation {valid ? 'Passed' : `(${errors.length} errors)`}
+        </span>
+        <Maximize2 size={14} className="text-dark-400" />
+      </div>
+    );
+  }
+
+  return (
+    <div className="bg-dark-850 border border-dark-700 rounded-xl w-96 max-h-[500px] flex flex-col shadow-xl">
+      {/* Header */}
+      <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700">
+        <div className="flex items-center gap-2">
+          {valid ? (
+            <CheckCircle size={18} className="text-green-400" />
+          ) : (
+            <AlertCircle size={18} className="text-red-400" />
+          )}
+          <span className="font-medium text-white">
+            {valid ? 'Validation Passed' : 'Validation Issues'}
+          </span>
+        </div>
+        <div className="flex items-center gap-1">
+          <button
+            onClick={() => minimizePanel('validation')}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+            title="Minimize"
+          >
+            <Minimize2 size={14} />
+          </button>
+          <button
+            onClick={onClose}
+            className="p-1.5 text-dark-400 hover:text-white hover:bg-dark-700 rounded transition-colors"
+          >
+            <X size={14} />
+          </button>
+        </div>
+      </div>
+
+      {/* Content */}
+      <div className="flex-1 overflow-y-auto p-3 space-y-2">
+        {valid && errors.length === 0 && warnings.length === 0 ? (
+          <div className="flex flex-col items-center justify-center py-8 text-center">
+            <CheckCircle size={40} className="text-green-400 mb-3" />
+            <p className="text-white font-medium">All checks passed!</p>
+            <p className="text-sm text-dark-400 mt-1">
+              Your spec is ready to run.
+            </p>
+          </div>
+        ) : (
+          <>
+            {/* Errors */}
+            {errors.length > 0 && (
+              <div className="space-y-2">
+                <h4 className="text-xs font-medium text-red-400 uppercase tracking-wide flex items-center gap-1">
+                  <AlertCircle size={12} />
+                  Errors ({errors.length})
+                </h4>
+                {errors.map((error, idx) => (
+                  <ValidationItem
+                    key={`error-${idx}`}
+                    item={error}
+                    severity="error"
+                    onNavigate={() => handleNavigateToNode(error.nodeId)}
+                  />
+                ))}
+              </div>
+            )}
+
+            {/* Warnings */}
+            {warnings.length > 0 && (
+              <div className="space-y-2 mt-4">
+                <h4 className="text-xs font-medium text-amber-400 uppercase tracking-wide flex items-center gap-1">
+                  <AlertTriangle size={12} />
+                  Warnings ({warnings.length})
+                </h4>
+                {warnings.map((warning, idx) => (
+                  <ValidationItem
+                    key={`warning-${idx}`}
+                    item={warning}
+                    severity="warning"
+                    onNavigate={() => handleNavigateToNode(warning.nodeId)}
+                  />
+                ))}
+              </div>
+            )}
+          </>
+        )}
+      </div>
+
+      {/* Footer */}
+      {!valid && (
+        <div className="px-4 py-3 border-t border-dark-700 bg-dark-800/50">
+          <p className="text-xs text-dark-400">
+            Fix all errors before running the optimization.
+            Warnings can be ignored but may cause issues.
+          </p>
+        </div>
+      )}
+    </div>
+  );
+}
+
+// ============================================================================
+// Validation Item Component
+// ============================================================================
+
+interface ValidationItemProps {
+  item: StoreValidationError;
+  severity: 'error' | 'warning';
+  onNavigate: () => void;
+}
+
+function ValidationItem({ item, severity, onNavigate }: ValidationItemProps) {
+  const isError = severity === 'error';
+  const bgColor = isError ? 'bg-red-500/10' : 'bg-amber-500/10';
+  const borderColor = isError ? 'border-red-500/30' : 'border-amber-500/30';
+  const iconColor = isError ? 'text-red-400' : 'text-amber-400';
+
+  return (
+    <div 
+      className={`p-3 rounded-lg border ${bgColor} ${borderColor} group cursor-pointer hover:bg-opacity-20 transition-colors`}
+      onClick={onNavigate}
+    >
+      <div className="flex items-start gap-2">
+        {isError ? (
+          <AlertCircle size={16} className={`${iconColor} flex-shrink-0 mt-0.5`} />
+        ) : (
+          <AlertTriangle size={16} className={`${iconColor} flex-shrink-0 mt-0.5`} />
+        )}
+        <div className="flex-1 min-w-0">
+          <p className="text-sm text-white">{item.message}</p>
+          {item.path && (
+            <p className="text-xs text-dark-400 mt-1 font-mono">{item.path}</p>
+          )}
+          {item.suggestion && (
+            <p className="text-xs text-dark-300 mt-2 italic">{item.suggestion}</p>
+          )}
+        </div>
+        {item.nodeId && (
+          <ChevronRight 
+            size={16} 
+            className="text-dark-500 group-hover:text-white transition-colors flex-shrink-0" 
+          />
+        )}
+      </div>
+    </div>
+  );
+}
+
+export default ValidationPanel;

atomizer-dashboard/frontend/src/components/canvas/visualization/ConvergenceSparkline.tsx

@@ -0,0 +1,240 @@
+/**
+ * ConvergenceSparkline - Tiny SVG chart showing optimization convergence
+ * 
+ * Displays the last N trial values as a mini line chart.
+ * Used on ObjectiveNode to show convergence trend.
+ */
+
+import { useMemo } from 'react';
+
+interface ConvergenceSparklineProps {
+  /** Array of values (most recent last) */
+  values: number[];
+  /** Width in pixels */
+  width?: number;
+  /** Height in pixels */
+  height?: number;
+  /** Line color */
+  color?: string;
+  /** Best value line color */
+  bestColor?: string;
+  /** Whether to show the best value line */
+  showBest?: boolean;
+  /** Direction: minimize shows lower as better, maximize shows higher as better */
+  direction?: 'minimize' | 'maximize';
+  /** Show dots at each point */
+  showDots?: boolean;
+  /** Number of points to display */
+  maxPoints?: number;
+}
+
+export function ConvergenceSparkline({
+  values,
+  width = 80,
+  height = 24,
+  color = '#60a5fa',
+  bestColor = '#34d399',
+  showBest = true,
+  direction = 'minimize',
+  showDots = false,
+  maxPoints = 20,
+}: ConvergenceSparklineProps) {
+  const { path, bestY, points } = useMemo(() => {
+    if (!values || values.length === 0) {
+      return { path: '', bestY: null, points: [], minVal: 0, maxVal: 1 };
+    }
+
+    // Take last N points
+    const data = values.slice(-maxPoints);
+    if (data.length === 0) {
+      return { path: '', bestY: null, points: [], minVal: 0, maxVal: 1 };
+    }
+
+    // Calculate bounds with padding
+    const minVal = Math.min(...data);
+    const maxVal = Math.max(...data);
+    const range = maxVal - minVal || 1;
+    const padding = range * 0.1;
+    const yMin = minVal - padding;
+    const yMax = maxVal + padding;
+    const yRange = yMax - yMin;
+
+    // Calculate best value
+    const bestVal = direction === 'minimize' ? Math.min(...data) : Math.max(...data);
+
+    // Map values to SVG coordinates
+    const xStep = width / Math.max(data.length - 1, 1);
+    const mapY = (v: number) => height - ((v - yMin) / yRange) * height;
+
+    // Build path
+    const points = data.map((v, i) => ({
+      x: i * xStep,
+      y: mapY(v),
+      value: v,
+    }));
+
+    const pathParts = points.map((p, i) => 
+      i === 0 ? `M ${p.x} ${p.y}` : `L ${p.x} ${p.y}`
+    );
+
+    return {
+      path: pathParts.join(' '),
+      bestY: mapY(bestVal),
+      points,
+      minVal,
+      maxVal,
+    };
+  }, [values, width, height, maxPoints, direction]);
+
+  if (!values || values.length === 0) {
+    return (
+      <div 
+        className="flex items-center justify-center text-dark-500 text-xs"
+        style={{ width, height }}
+      >
+        No data
+      </div>
+    );
+  }
+
+  return (
+    <svg 
+      width={width} 
+      height={height} 
+      className="overflow-visible"
+      viewBox={`0 0 ${width} ${height}`}
+    >
+      {/* Best value line */}
+      {showBest && bestY !== null && (
+        <line
+          x1={0}
+          y1={bestY}
+          x2={width}
+          y2={bestY}
+          stroke={bestColor}
+          strokeWidth={1}
+          strokeDasharray="2,2"
+          opacity={0.5}
+        />
+      )}
+
+      {/* Main line */}
+      <path
+        d={path}
+        fill="none"
+        stroke={color}
+        strokeWidth={1.5}
+        strokeLinecap="round"
+        strokeLinejoin="round"
+      />
+
+      {/* Gradient fill under the line */}
+      <defs>
+        <linearGradient id="sparkline-gradient" x1="0%" y1="0%" x2="0%" y2="100%">
+          <stop offset="0%" stopColor={color} stopOpacity={0.3} />
+          <stop offset="100%" stopColor={color} stopOpacity={0} />
+        </linearGradient>
+      </defs>
+      
+      {points.length > 1 && (
+        <path
+          d={`${path} L ${points[points.length - 1].x} ${height} L ${points[0].x} ${height} Z`}
+          fill="url(#sparkline-gradient)"
+        />
+      )}
+
+      {/* Dots at each point */}
+      {showDots && points.map((p, i) => (
+        <circle
+          key={i}
+          cx={p.x}
+          cy={p.y}
+          r={2}
+          fill={color}
+        />
+      ))}
+
+      {/* Last point highlight */}
+      {points.length > 0 && (
+        <circle
+          cx={points[points.length - 1].x}
+          cy={points[points.length - 1].y}
+          r={3}
+          fill={color}
+          stroke="white"
+          strokeWidth={1}
+        />
+      )}
+    </svg>
+  );
+}
+
+/**
+ * ProgressRing - Circular progress indicator
+ */
+interface ProgressRingProps {
+  /** Progress percentage (0-100) */
+  progress: number;
+  /** Size in pixels */
+  size?: number;
+  /** Stroke width */
+  strokeWidth?: number;
+  /** Progress color */
+  color?: string;
+  /** Background color */
+  bgColor?: string;
+  /** Show percentage text */
+  showText?: boolean;
+}
+
+export function ProgressRing({
+  progress,
+  size = 32,
+  strokeWidth = 3,
+  color = '#60a5fa',
+  bgColor = '#374151',
+  showText = true,
+}: ProgressRingProps) {
+  const radius = (size - strokeWidth) / 2;
+  const circumference = radius * 2 * Math.PI;
+  const offset = circumference - (Math.min(100, Math.max(0, progress)) / 100) * circumference;
+
+  return (
+    <div className="relative inline-flex items-center justify-center" style={{ width: size, height: size }}>
+      <svg width={size} height={size} className="transform -rotate-90">
+        {/* Background circle */}
+        <circle
+          cx={size / 2}
+          cy={size / 2}
+          r={radius}
+          fill="none"
+          stroke={bgColor}
+          strokeWidth={strokeWidth}
+        />
+        {/* Progress circle */}
+        <circle
+          cx={size / 2}
+          cy={size / 2}
+          r={radius}
+          fill="none"
+          stroke={color}
+          strokeWidth={strokeWidth}
+          strokeDasharray={circumference}
+          strokeDashoffset={offset}
+          strokeLinecap="round"
+          className="transition-all duration-300"
+        />
+      </svg>
+      {showText && (
+        <span 
+          className="absolute text-xs font-medium"
+          style={{ color, fontSize: size * 0.25 }}
+        >
+          {Math.round(progress)}%
+        </span>
+      )}
+    </div>
+  );
+}
+
+export default ConvergenceSparkline;

atomizer-dashboard/frontend/src/components/devloop/DevLoopPanel.tsx

@@ -0,0 +1,342 @@
+/**
+ * DevLoopPanel - Control panel for closed-loop development
+ * 
+ * Features:
+ * - Start/stop development cycles
+ * - Real-time phase monitoring
+ * - Iteration history view
+ * - Test result visualization
+ */
+
+import { useState, useEffect, useCallback } from 'react';
+import {
+  PlayCircle,
+  StopCircle,
+  RefreshCw,
+  CheckCircle,
+  XCircle,
+  AlertCircle,
+  Clock,
+  ListChecks,
+  Zap,
+  ChevronDown,
+  ChevronRight,
+} from 'lucide-react';
+import useWebSocket from 'react-use-websocket';
+
+interface LoopState {
+  phase: string;
+  iteration: number;
+  current_task: string | null;
+  last_update: string;
+}
+
+interface CycleResult {
+  objective: string;
+  status: string;
+  iterations: number;
+  duration_seconds: number;
+}
+
+interface TestResult {
+  scenario_id: string;
+  scenario_name: string;
+  passed: boolean;
+  duration_ms: number;
+  error?: string;
+}
+
+const PHASE_COLORS: Record<string, string> = {
+  idle: 'bg-gray-500',
+  planning: 'bg-blue-500',
+  implementing: 'bg-purple-500',
+  testing: 'bg-yellow-500',
+  analyzing: 'bg-orange-500',
+  fixing: 'bg-red-500',
+  verifying: 'bg-green-500',
+};
+
+const PHASE_ICONS: Record<string, React.ReactNode> = {
+  idle: <Clock className="w-4 h-4" />,
+  planning: <ListChecks className="w-4 h-4" />,
+  implementing: <Zap className="w-4 h-4" />,
+  testing: <RefreshCw className="w-4 h-4 animate-spin" />,
+  analyzing: <AlertCircle className="w-4 h-4" />,
+  fixing: <Zap className="w-4 h-4" />,
+  verifying: <CheckCircle className="w-4 h-4" />,
+};
+
+export function DevLoopPanel() {
+  const [state, setState] = useState<LoopState>({
+    phase: 'idle',
+    iteration: 0,
+    current_task: null,
+    last_update: new Date().toISOString(),
+  });
+  const [objective, setObjective] = useState('');
+  const [history, setHistory] = useState<CycleResult[]>([]);
+  const [testResults, setTestResults] = useState<TestResult[]>([]);
+  const [expanded, setExpanded] = useState(true);
+  const [isStarting, setIsStarting] = useState(false);
+
+  // WebSocket connection for real-time updates
+  const { lastJsonMessage, readyState } = useWebSocket(
+    'ws://localhost:8000/api/devloop/ws',
+    {
+      shouldReconnect: () => true,
+      reconnectInterval: 3000,
+    }
+  );
+
+  // Handle WebSocket messages
+  useEffect(() => {
+    if (!lastJsonMessage) return;
+
+    const msg = lastJsonMessage as any;
+
+    switch (msg.type) {
+      case 'connection_ack':
+      case 'state_update':
+      case 'state':
+        if (msg.state) {
+          setState(msg.state);
+        }
+        break;
+      case 'cycle_complete':
+        setHistory(prev => [msg.result, ...prev].slice(0, 10));
+        setIsStarting(false);
+        break;
+      case 'cycle_error':
+        console.error('DevLoop error:', msg.error);
+        setIsStarting(false);
+        break;
+      case 'test_progress':
+        if (msg.result) {
+          setTestResults(prev => [...prev, msg.result]);
+        }
+        break;
+    }
+  }, [lastJsonMessage]);
+
+  // Start a development cycle
+  const startCycle = useCallback(async () => {
+    if (!objective.trim()) return;
+
+    setIsStarting(true);
+    setTestResults([]);
+
+    try {
+      const response = await fetch('http://localhost:8000/api/devloop/start', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          objective: objective.trim(),
+          max_iterations: 10,
+        }),
+      });
+
+      if (!response.ok) {
+        const error = await response.json();
+        console.error('Failed to start cycle:', error);
+        setIsStarting(false);
+      }
+    } catch (error) {
+      console.error('Failed to start cycle:', error);
+      setIsStarting(false);
+    }
+  }, [objective]);
+
+  // Stop the current cycle
+  const stopCycle = useCallback(async () => {
+    try {
+      await fetch('http://localhost:8000/api/devloop/stop', {
+        method: 'POST',
+      });
+    } catch (error) {
+      console.error('Failed to stop cycle:', error);
+    }
+  }, []);
+
+  // Quick start: Create support_arm study
+  const quickStartSupportArm = useCallback(() => {
+    setObjective('Create support_arm optimization study with 5 design variables (center_space, arm_thk, arm_angle, end_thk, base_thk), objectives (minimize displacement, minimize mass), and stress constraint (< 30% yield)');
+    // Auto-start after a brief delay
+    setTimeout(() => {
+      startCycle();
+    }, 500);
+  }, [startCycle]);
+
+  const isActive = state.phase !== 'idle';
+  const wsConnected = readyState === WebSocket.OPEN;
+
+  return (
+    <div className="bg-gray-900 rounded-lg border border-gray-700 overflow-hidden">
+      {/* Header */}
+      <div
+        className="flex items-center justify-between px-4 py-3 bg-gray-800 cursor-pointer"
+        onClick={() => setExpanded(!expanded)}
+      >
+        <div className="flex items-center gap-2">
+          {expanded ? (
+            <ChevronDown className="w-4 h-4 text-gray-400" />
+          ) : (
+            <ChevronRight className="w-4 h-4 text-gray-400" />
+          )}
+          <RefreshCw className="w-5 h-5 text-blue-400" />
+          <h3 className="font-semibold text-white">DevLoop Control</h3>
+        </div>
+        
+        {/* Status indicator */}
+        <div className="flex items-center gap-2">
+          <div
+            className={`w-2 h-2 rounded-full ${
+              wsConnected ? 'bg-green-500' : 'bg-red-500'
+            }`}
+          />
+          <span className={`px-2 py-1 text-xs rounded ${PHASE_COLORS[state.phase]} text-white`}>
+            {state.phase.toUpperCase()}
+          </span>
+        </div>
+      </div>
+
+      {expanded && (
+        <div className="p-4 space-y-4">
+          {/* Objective Input */}
+          <div>
+            <label className="block text-sm text-gray-400 mb-1">
+              Development Objective
+            </label>
+            <textarea
+              value={objective}
+              onChange={(e) => setObjective(e.target.value)}
+              placeholder="e.g., Create support_arm optimization study..."
+              className="w-full px-3 py-2 bg-gray-800 border border-gray-600 rounded text-white text-sm resize-none h-20"
+              disabled={isActive}
+            />
+          </div>
+
+          {/* Quick Actions */}
+          <div className="flex gap-2">
+            <button
+              onClick={quickStartSupportArm}
+              disabled={isActive}
+              className="px-3 py-1.5 bg-purple-600 hover:bg-purple-700 disabled:bg-gray-600 text-white text-sm rounded flex items-center gap-1"
+            >
+              <Zap className="w-4 h-4" />
+              Quick: support_arm
+            </button>
+          </div>
+
+          {/* Control Buttons */}
+          <div className="flex gap-2">
+            {!isActive ? (
+              <button
+                onClick={startCycle}
+                disabled={!objective.trim() || isStarting}
+                className="flex-1 px-4 py-2 bg-green-600 hover:bg-green-700 disabled:bg-gray-600 text-white rounded flex items-center justify-center gap-2"
+              >
+                <PlayCircle className="w-5 h-5" />
+                {isStarting ? 'Starting...' : 'Start Cycle'}
+              </button>
+            ) : (
+              <button
+                onClick={stopCycle}
+                className="flex-1 px-4 py-2 bg-red-600 hover:bg-red-700 text-white rounded flex items-center justify-center gap-2"
+              >
+                <StopCircle className="w-5 h-5" />
+                Stop Cycle
+              </button>
+            )}
+          </div>
+
+          {/* Current Phase Progress */}
+          {isActive && (
+            <div className="bg-gray-800 rounded p-3 space-y-2">
+              <div className="flex items-center gap-2">
+                {PHASE_ICONS[state.phase]}
+                <span className="text-sm text-white font-medium">
+                  {state.phase.charAt(0).toUpperCase() + state.phase.slice(1)}
+                </span>
+                <span className="text-xs text-gray-400">
+                  Iteration {state.iteration + 1}
+                </span>
+              </div>
+              {state.current_task && (
+                <p className="text-xs text-gray-400 truncate">
+                  {state.current_task}
+                </p>
+              )}
+            </div>
+          )}
+
+          {/* Test Results */}
+          {testResults.length > 0 && (
+            <div className="bg-gray-800 rounded p-3">
+              <h4 className="text-sm font-medium text-white mb-2">Test Results</h4>
+              <div className="space-y-1 max-h-32 overflow-y-auto">
+                {testResults.map((test, i) => (
+                  <div
+                    key={`${test.scenario_id}-${i}`}
+                    className="flex items-center gap-2 text-xs"
+                  >
+                    {test.passed ? (
+                      <CheckCircle className="w-3 h-3 text-green-500" />
+                    ) : (
+                      <XCircle className="w-3 h-3 text-red-500" />
+                    )}
+                    <span className="text-gray-300 truncate flex-1">
+                      {test.scenario_name}
+                    </span>
+                    <span className="text-gray-500">
+                      {test.duration_ms.toFixed(0)}ms
+                    </span>
+                  </div>
+                ))}
+              </div>
+            </div>
+          )}
+
+          {/* History */}
+          {history.length > 0 && (
+            <div className="bg-gray-800 rounded p-3">
+              <h4 className="text-sm font-medium text-white mb-2">Recent Cycles</h4>
+              <div className="space-y-2">
+                {history.slice(0, 3).map((cycle, i) => (
+                  <div
+                    key={i}
+                    className="flex items-center justify-between text-xs"
+                  >
+                    <span className="text-gray-300 truncate flex-1">
+                      {cycle.objective.substring(0, 40)}...
+                    </span>
+                    <span
+                      className={`px-1.5 py-0.5 rounded ${
+                        cycle.status === 'completed'
+                          ? 'bg-green-900 text-green-300'
+                          : 'bg-yellow-900 text-yellow-300'
+                      }`}
+                    >
+                      {cycle.status}
+                    </span>
+                  </div>
+                ))}
+              </div>
+            </div>
+          )}
+
+          {/* Phase Legend */}
+          <div className="grid grid-cols-4 gap-2 text-xs">
+            {Object.entries(PHASE_COLORS).map(([phase, color]) => (
+              <div key={phase} className="flex items-center gap-1">
+                <div className={`w-2 h-2 rounded ${color}`} />
+                <span className="text-gray-400 capitalize">{phase}</span>
+              </div>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
+
+export default DevLoopPanel;

atomizer-dashboard/frontend/src/components/intake/ContextFileUpload.tsx

@@ -0,0 +1,292 @@
+/**
+ * ContextFileUpload - Upload context files for study configuration
+ *
+ * Allows uploading markdown, text, PDF, and image files that help
+ * Claude understand optimization goals and generate better documentation.
+ */
+
+import React, { useState, useEffect, useRef, useCallback } from 'react';
+import { Upload, FileText, X, Loader2, AlertCircle, CheckCircle, Trash2, BookOpen } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface ContextFileUploadProps {
+  studyName: string;
+  onUploadComplete: () => void;
+}
+
+interface ContextFile {
+  name: string;
+  path: string;
+  size: number;
+  extension: string;
+}
+
+interface FileStatus {
+  file: File;
+  status: 'pending' | 'uploading' | 'success' | 'error';
+  message?: string;
+}
+
+const VALID_EXTENSIONS = ['.md', '.txt', '.pdf', '.png', '.jpg', '.jpeg', '.json', '.csv'];
+
+export const ContextFileUpload: React.FC<ContextFileUploadProps> = ({
+  studyName,
+  onUploadComplete,
+}) => {
+  const [contextFiles, setContextFiles] = useState<ContextFile[]>([]);
+  const [pendingFiles, setPendingFiles] = useState<FileStatus[]>([]);
+  const [isUploading, setIsUploading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+  // Load existing context files
+  const loadContextFiles = useCallback(async () => {
+    try {
+      const response = await intakeApi.listContextFiles(studyName);
+      setContextFiles(response.context_files);
+    } catch (err) {
+      console.error('Failed to load context files:', err);
+    }
+  }, [studyName]);
+
+  useEffect(() => {
+    loadContextFiles();
+  }, [loadContextFiles]);
+
+  const validateFile = (file: File): { valid: boolean; reason?: string } => {
+    const ext = '.' + file.name.split('.').pop()?.toLowerCase();
+    if (!VALID_EXTENSIONS.includes(ext)) {
+      return { valid: false, reason: `Invalid type: ${ext}` };
+    }
+    // Max 10MB per file
+    if (file.size > 10 * 1024 * 1024) {
+      return { valid: false, reason: 'File too large (max 10MB)' };
+    }
+    return { valid: true };
+  };
+
+  const addFiles = useCallback((newFiles: File[]) => {
+    const validFiles: FileStatus[] = [];
+    
+    for (const file of newFiles) {
+      // Skip duplicates
+      if (pendingFiles.some(f => f.file.name === file.name)) {
+        continue;
+      }
+      if (contextFiles.some(f => f.name === file.name)) {
+        continue;
+      }
+      
+      const validation = validateFile(file);
+      if (validation.valid) {
+        validFiles.push({ file, status: 'pending' });
+      } else {
+        validFiles.push({ file, status: 'error', message: validation.reason });
+      }
+    }
+    
+    setPendingFiles(prev => [...prev, ...validFiles]);
+  }, [pendingFiles, contextFiles]);
+
+  const handleFileSelect = useCallback((e: React.ChangeEvent<HTMLInputElement>) => {
+    const selectedFiles = Array.from(e.target.files || []);
+    addFiles(selectedFiles);
+    e.target.value = '';
+  }, [addFiles]);
+
+  const removeFile = (index: number) => {
+    setPendingFiles(prev => prev.filter((_, i) => i !== index));
+  };
+
+  const handleUpload = async () => {
+    const filesToUpload = pendingFiles.filter(f => f.status === 'pending');
+    if (filesToUpload.length === 0) return;
+
+    setIsUploading(true);
+    setError(null);
+
+    try {
+      const response = await intakeApi.uploadContextFiles(
+        studyName,
+        filesToUpload.map(f => f.file)
+      );
+
+      // Update pending file statuses
+      const uploadResults = new Map(
+        response.uploaded_files.map(f => [f.name, f.status === 'uploaded'])
+      );
+
+      setPendingFiles(prev => prev.map(f => {
+        if (f.status !== 'pending') return f;
+        const success = uploadResults.get(f.file.name);
+        return {
+          ...f,
+          status: success ? 'success' : 'error',
+          message: success ? undefined : 'Upload failed',
+        };
+      }));
+
+      // Refresh and clear after a moment
+      setTimeout(() => {
+        setPendingFiles(prev => prev.filter(f => f.status !== 'success'));
+        loadContextFiles();
+        onUploadComplete();
+      }, 1500);
+
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Upload failed');
+    } finally {
+      setIsUploading(false);
+    }
+  };
+
+  const handleDeleteFile = async (filename: string) => {
+    try {
+      await intakeApi.deleteContextFile(studyName, filename);
+      loadContextFiles();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Delete failed');
+    }
+  };
+
+  const pendingCount = pendingFiles.filter(f => f.status === 'pending').length;
+
+  const formatSize = (bytes: number) => {
+    if (bytes < 1024) return `${bytes} B`;
+    if (bytes < 1024 * 1024) return `${(bytes / 1024).toFixed(1)} KB`;
+    return `${(bytes / 1024 / 1024).toFixed(1)} MB`;
+  };
+
+  return (
+    <div className="space-y-3">
+      <div className="flex items-center justify-between">
+        <h5 className="text-sm font-medium text-dark-300 flex items-center gap-2">
+          <BookOpen className="w-4 h-4 text-purple-400" />
+          Context Files
+        </h5>
+        <button
+          onClick={() => fileInputRef.current?.click()}
+          className="flex items-center gap-1.5 px-2 py-1 rounded text-xs font-medium
+                     bg-purple-500/10 text-purple-400 hover:bg-purple-500/20
+                     transition-colors"
+        >
+          <Upload className="w-3 h-3" />
+          Add Context
+        </button>
+      </div>
+
+      <p className="text-xs text-dark-500">
+        Add .md, .txt, or .pdf files describing your optimization goals. Claude will use these to generate documentation.
+      </p>
+
+      {/* Error Display */}
+      {error && (
+        <div className="p-2 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-xs flex items-center gap-2">
+          <AlertCircle className="w-3 h-3 flex-shrink-0" />
+          {error}
+          <button onClick={() => setError(null)} className="ml-auto hover:text-white">
+            <X className="w-3 h-3" />
+          </button>
+        </div>
+      )}
+
+      {/* Existing Context Files */}
+      {contextFiles.length > 0 && (
+        <div className="space-y-1">
+          {contextFiles.map((file) => (
+            <div
+              key={file.name}
+              className="flex items-center justify-between p-2 rounded-lg bg-purple-500/5 border border-purple-500/20"
+            >
+              <div className="flex items-center gap-2">
+                <FileText className="w-4 h-4 text-purple-400" />
+                <span className="text-sm text-white">{file.name}</span>
+                <span className="text-xs text-dark-500">{formatSize(file.size)}</span>
+              </div>
+              <button
+                onClick={() => handleDeleteFile(file.name)}
+                className="p-1 hover:bg-white/10 rounded text-dark-400 hover:text-red-400"
+                title="Delete file"
+              >
+                <Trash2 className="w-3 h-3" />
+              </button>
+            </div>
+          ))}
+        </div>
+      )}
+
+      {/* Pending Files */}
+      {pendingFiles.length > 0 && (
+        <div className="space-y-1">
+          {pendingFiles.map((f, i) => (
+            <div
+              key={i}
+              className={`flex items-center justify-between p-2 rounded-lg
+                         ${f.status === 'error' ? 'bg-red-500/10' : 
+                           f.status === 'success' ? 'bg-green-500/10' :
+                           'bg-dark-700'}`}
+            >
+              <div className="flex items-center gap-2">
+                {f.status === 'pending' && <FileText className="w-4 h-4 text-dark-400" />}
+                {f.status === 'uploading' && <Loader2 className="w-4 h-4 text-purple-400 animate-spin" />}
+                {f.status === 'success' && <CheckCircle className="w-4 h-4 text-green-400" />}
+                {f.status === 'error' && <AlertCircle className="w-4 h-4 text-red-400" />}
+                <span className={`text-sm ${f.status === 'error' ? 'text-red-400' : 
+                                            f.status === 'success' ? 'text-green-400' : 
+                                            'text-white'}`}>
+                  {f.file.name}
+                </span>
+                {f.message && (
+                  <span className="text-xs text-red-400">({f.message})</span>
+                )}
+              </div>
+              {f.status === 'pending' && (
+                <button
+                  onClick={() => removeFile(i)}
+                  className="p-1 hover:bg-white/10 rounded text-dark-400 hover:text-white"
+                >
+                  <X className="w-3 h-3" />
+                </button>
+              )}
+            </div>
+          ))}
+        </div>
+      )}
+
+      {/* Upload Button */}
+      {pendingCount > 0 && (
+        <button
+          onClick={handleUpload}
+          disabled={isUploading}
+          className="w-full flex items-center justify-center gap-2 px-3 py-2 rounded-lg
+                     bg-purple-500 text-white text-sm font-medium
+                     hover:bg-purple-400 disabled:opacity-50 disabled:cursor-not-allowed
+                     transition-colors"
+        >
+          {isUploading ? (
+            <>
+              <Loader2 className="w-4 h-4 animate-spin" />
+              Uploading...
+            </>
+          ) : (
+            <>
+              <Upload className="w-4 h-4" />
+              Upload {pendingCount} {pendingCount === 1 ? 'File' : 'Files'}
+            </>
+          )}
+        </button>
+      )}
+
+      <input
+        ref={fileInputRef}
+        type="file"
+        multiple
+        accept={VALID_EXTENSIONS.join(',')}
+        onChange={handleFileSelect}
+        className="hidden"
+      />
+    </div>
+  );
+};
+
+export default ContextFileUpload;

atomizer-dashboard/frontend/src/components/intake/CreateStudyCard.tsx

@@ -0,0 +1,227 @@
+/**
+ * CreateStudyCard - Card for initiating new study creation
+ *
+ * Displays a prominent card on the Home page that allows users to
+ * create a new study through the intake workflow.
+ */
+
+import React, { useState } from 'react';
+import { Plus, Loader2 } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+import { TopicInfo } from '../../types/intake';
+
+interface CreateStudyCardProps {
+  topics: TopicInfo[];
+  onStudyCreated: (studyName: string) => void;
+}
+
+export const CreateStudyCard: React.FC<CreateStudyCardProps> = ({
+  topics,
+  onStudyCreated,
+}) => {
+  const [isExpanded, setIsExpanded] = useState(false);
+  const [studyName, setStudyName] = useState('');
+  const [description, setDescription] = useState('');
+  const [selectedTopic, setSelectedTopic] = useState('');
+  const [newTopic, setNewTopic] = useState('');
+  const [isCreating, setIsCreating] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  const handleCreate = async () => {
+    if (!studyName.trim()) {
+      setError('Study name is required');
+      return;
+    }
+
+    // Validate study name format
+    const nameRegex = /^[a-z0-9_]+$/;
+    if (!nameRegex.test(studyName)) {
+      setError('Study name must be lowercase with underscores only (e.g., my_study_name)');
+      return;
+    }
+
+    setIsCreating(true);
+    setError(null);
+
+    try {
+      const topic = newTopic.trim() || selectedTopic || undefined;
+      await intakeApi.createInbox({
+        study_name: studyName.trim(),
+        description: description.trim() || undefined,
+        topic,
+      });
+
+      // Reset form
+      setStudyName('');
+      setDescription('');
+      setSelectedTopic('');
+      setNewTopic('');
+      setIsExpanded(false);
+
+      onStudyCreated(studyName.trim());
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Failed to create study');
+    } finally {
+      setIsCreating(false);
+    }
+  };
+
+  if (!isExpanded) {
+    return (
+      <button
+        onClick={() => setIsExpanded(true)}
+        className="w-full glass rounded-xl p-6 border border-dashed border-primary-400/30 
+                   hover:border-primary-400/60 hover:bg-primary-400/5 transition-all
+                   flex items-center justify-center gap-3 group"
+      >
+        <div className="w-12 h-12 rounded-xl bg-primary-400/10 flex items-center justify-center
+                        group-hover:bg-primary-400/20 transition-colors">
+          <Plus className="w-6 h-6 text-primary-400" />
+        </div>
+        <div className="text-left">
+          <h3 className="text-lg font-semibold text-white">Create New Study</h3>
+          <p className="text-sm text-dark-400">Set up a new optimization study</p>
+        </div>
+      </button>
+    );
+  }
+
+  return (
+    <div className="glass-strong rounded-xl border border-primary-400/20 overflow-hidden">
+      {/* Header */}
+      <div className="px-6 py-4 border-b border-primary-400/10 flex items-center justify-between">
+        <div className="flex items-center gap-3">
+          <div className="w-10 h-10 rounded-lg bg-primary-400/10 flex items-center justify-center">
+            <Plus className="w-5 h-5 text-primary-400" />
+          </div>
+          <h3 className="text-lg font-semibold text-white">Create New Study</h3>
+        </div>
+        <button
+          onClick={() => setIsExpanded(false)}
+          className="text-dark-400 hover:text-white transition-colors text-sm"
+        >
+          Cancel
+        </button>
+      </div>
+
+      {/* Form */}
+      <div className="p-6 space-y-4">
+        {/* Study Name */}
+        <div>
+          <label className="block text-sm font-medium text-dark-300 mb-2">
+            Study Name <span className="text-red-400">*</span>
+          </label>
+          <input
+            type="text"
+            value={studyName}
+            onChange={(e) => setStudyName(e.target.value.toLowerCase().replace(/[^a-z0-9_]/g, '_'))}
+            placeholder="my_optimization_study"
+            className="w-full px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                       text-white placeholder-dark-500 focus:border-primary-400 
+                       focus:outline-none focus:ring-1 focus:ring-primary-400/50"
+          />
+          <p className="mt-1 text-xs text-dark-500">
+            Lowercase letters, numbers, and underscores only
+          </p>
+        </div>
+
+        {/* Description */}
+        <div>
+          <label className="block text-sm font-medium text-dark-300 mb-2">
+            Description
+          </label>
+          <textarea
+            value={description}
+            onChange={(e) => setDescription(e.target.value)}
+            placeholder="Brief description of the optimization goal..."
+            rows={2}
+            className="w-full px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                       text-white placeholder-dark-500 focus:border-primary-400 
+                       focus:outline-none focus:ring-1 focus:ring-primary-400/50 resize-none"
+          />
+        </div>
+
+        {/* Topic Selection */}
+        <div>
+          <label className="block text-sm font-medium text-dark-300 mb-2">
+            Topic Folder
+          </label>
+          <div className="flex gap-2">
+            <select
+              value={selectedTopic}
+              onChange={(e) => {
+                setSelectedTopic(e.target.value);
+                setNewTopic('');
+              }}
+              className="flex-1 px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                         text-white focus:border-primary-400 focus:outline-none 
+                         focus:ring-1 focus:ring-primary-400/50"
+            >
+              <option value="">Select existing topic...</option>
+              {topics.map((topic) => (
+                <option key={topic.name} value={topic.name}>
+                  {topic.name} ({topic.study_count} studies)
+                </option>
+              ))}
+            </select>
+            <span className="text-dark-500 self-center">or</span>
+            <input
+              type="text"
+              value={newTopic}
+              onChange={(e) => {
+                setNewTopic(e.target.value.replace(/[^A-Za-z0-9_]/g, '_'));
+                setSelectedTopic('');
+              }}
+              placeholder="New_Topic"
+              className="flex-1 px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                         text-white placeholder-dark-500 focus:border-primary-400 
+                         focus:outline-none focus:ring-1 focus:ring-primary-400/50"
+            />
+          </div>
+        </div>
+
+        {/* Error Message */}
+        {error && (
+          <div className="p-3 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-sm">
+            {error}
+          </div>
+        )}
+
+        {/* Actions */}
+        <div className="flex justify-end gap-3 pt-2">
+          <button
+            onClick={() => setIsExpanded(false)}
+            className="px-4 py-2 rounded-lg border border-dark-600 text-dark-300 
+                       hover:border-dark-500 hover:text-white transition-colors"
+          >
+            Cancel
+          </button>
+          <button
+            onClick={handleCreate}
+            disabled={isCreating || !studyName.trim()}
+            className="px-6 py-2 rounded-lg font-medium transition-all disabled:opacity-50
+                       flex items-center gap-2"
+            style={{
+              background: 'linear-gradient(135deg, #00d4e6 0%, #0891b2 100%)',
+              color: '#000',
+            }}
+          >
+            {isCreating ? (
+              <>
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Creating...
+              </>
+            ) : (
+              <>
+                <Plus className="w-4 h-4" />
+                Create Study
+              </>
+            )}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+};
+
+export default CreateStudyCard;

atomizer-dashboard/frontend/src/components/intake/ExpressionList.tsx

@@ -0,0 +1,270 @@
+/**
+ * ExpressionList - Display discovered expressions with selection capability
+ *
+ * Shows expressions from NX introspection, allowing users to:
+ * - View all discovered expressions
+ * - See which are design variable candidates (auto-detected)
+ * - Select/deselect expressions to use as design variables
+ * - View expression values and units
+ */
+
+import React, { useState } from 'react';
+import {
+  Check,
+  Search,
+  AlertTriangle,
+  Sparkles,
+  Info,
+  Variable,
+} from 'lucide-react';
+import { ExpressionInfo } from '../../types/intake';
+
+interface ExpressionListProps {
+  /** Expression data from introspection */
+  expressions: ExpressionInfo[];
+  /** Mass from introspection (kg) */
+  massKg?: number | null;
+  /** Currently selected expressions (to become DVs) */
+  selectedExpressions: string[];
+  /** Callback when selection changes */
+  onSelectionChange: (selected: string[]) => void;
+  /** Whether in read-only mode */
+  readOnly?: boolean;
+  /** Compact display mode */
+  compact?: boolean;
+}
+
+export const ExpressionList: React.FC<ExpressionListProps> = ({
+  expressions,
+  massKg,
+  selectedExpressions,
+  onSelectionChange,
+  readOnly = false,
+  compact = false,
+}) => {
+  const [filter, setFilter] = useState('');
+  const [showCandidatesOnly, setShowCandidatesOnly] = useState(true);
+
+  // Filter expressions based on search and candidate toggle
+  const filteredExpressions = expressions.filter((expr) => {
+    const matchesSearch = filter === '' || 
+      expr.name.toLowerCase().includes(filter.toLowerCase());
+    const matchesCandidate = !showCandidatesOnly || expr.is_candidate;
+    return matchesSearch && matchesCandidate;
+  });
+
+  // Sort: candidates first, then by confidence, then alphabetically
+  const sortedExpressions = [...filteredExpressions].sort((a, b) => {
+    if (a.is_candidate !== b.is_candidate) {
+      return a.is_candidate ? -1 : 1;
+    }
+    if (a.confidence !== b.confidence) {
+      return b.confidence - a.confidence;
+    }
+    return a.name.localeCompare(b.name);
+  });
+
+  const toggleExpression = (name: string) => {
+    if (readOnly) return;
+    
+    if (selectedExpressions.includes(name)) {
+      onSelectionChange(selectedExpressions.filter(n => n !== name));
+    } else {
+      onSelectionChange([...selectedExpressions, name]);
+    }
+  };
+
+  const selectAllCandidates = () => {
+    const candidateNames = expressions
+      .filter(e => e.is_candidate)
+      .map(e => e.name);
+    onSelectionChange(candidateNames);
+  };
+
+  const clearSelection = () => {
+    onSelectionChange([]);
+  };
+
+  const candidateCount = expressions.filter(e => e.is_candidate).length;
+
+  if (expressions.length === 0) {
+    return (
+      <div className="p-4 rounded-lg bg-dark-700/50 border border-dark-600">
+        <div className="flex items-center gap-2 text-dark-400">
+          <AlertTriangle className="w-4 h-4" />
+          <span>No expressions found. Run introspection to discover model parameters.</span>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="space-y-3">
+      {/* Header with stats */}
+      <div className="flex items-center justify-between">
+        <div className="flex items-center gap-3">
+          <h5 className="text-sm font-medium text-dark-300 flex items-center gap-2">
+            <Variable className="w-4 h-4" />
+            Discovered Expressions
+          </h5>
+          <span className="text-xs text-dark-500">
+            {expressions.length} total, {candidateCount} candidates
+          </span>
+          {massKg && (
+            <span className="text-xs text-primary-400">
+              Mass: {massKg.toFixed(3)} kg
+            </span>
+          )}
+        </div>
+        {!readOnly && selectedExpressions.length > 0 && (
+          <span className="text-xs text-green-400">
+            {selectedExpressions.length} selected
+          </span>
+        )}
+      </div>
+
+      {/* Controls */}
+      {!compact && (
+        <div className="flex items-center gap-3">
+          {/* Search */}
+          <div className="relative flex-1 max-w-xs">
+            <Search className="absolute left-2.5 top-1/2 -translate-y-1/2 w-4 h-4 text-dark-500" />
+            <input
+              type="text"
+              placeholder="Search expressions..."
+              value={filter}
+              onChange={(e) => setFilter(e.target.value)}
+              className="w-full pl-8 pr-3 py-1.5 text-sm rounded-lg bg-dark-700 border border-dark-600
+                         text-white placeholder-dark-500 focus:border-primary-500/50 focus:outline-none"
+            />
+          </div>
+
+          {/* Show candidates only toggle */}
+          <label className="flex items-center gap-2 text-xs text-dark-400 cursor-pointer">
+            <input
+              type="checkbox"
+              checked={showCandidatesOnly}
+              onChange={(e) => setShowCandidatesOnly(e.target.checked)}
+              className="w-4 h-4 rounded border-dark-500 bg-dark-700 text-primary-500 
+                         focus:ring-primary-500/30"
+            />
+            Candidates only
+          </label>
+
+          {/* Quick actions */}
+          {!readOnly && (
+            <div className="flex items-center gap-2">
+              <button
+                onClick={selectAllCandidates}
+                className="px-2 py-1 text-xs rounded bg-primary-500/10 text-primary-400 
+                           hover:bg-primary-500/20 transition-colors"
+              >
+                Select all candidates
+              </button>
+              <button
+                onClick={clearSelection}
+                className="px-2 py-1 text-xs rounded bg-dark-600 text-dark-400 
+                           hover:bg-dark-500 transition-colors"
+              >
+                Clear
+              </button>
+            </div>
+          )}
+        </div>
+      )}
+
+      {/* Expression list */}
+      <div className={`rounded-lg border border-dark-600 overflow-hidden ${
+        compact ? 'max-h-48' : 'max-h-72'
+      } overflow-y-auto`}>
+        <table className="w-full text-sm">
+          <thead className="bg-dark-700 sticky top-0">
+            <tr>
+              {!readOnly && (
+                <th className="w-8 px-2 py-2"></th>
+              )}
+              <th className="px-3 py-2 text-left text-dark-400 font-medium">Name</th>
+              <th className="px-3 py-2 text-right text-dark-400 font-medium w-24">Value</th>
+              <th className="px-3 py-2 text-left text-dark-400 font-medium w-16">Units</th>
+              <th className="px-3 py-2 text-center text-dark-400 font-medium w-20">Candidate</th>
+            </tr>
+          </thead>
+          <tbody className="divide-y divide-dark-700">
+            {sortedExpressions.map((expr) => {
+              const isSelected = selectedExpressions.includes(expr.name);
+              return (
+                <tr
+                  key={expr.name}
+                  onClick={() => toggleExpression(expr.name)}
+                  className={`
+                    ${readOnly ? '' : 'cursor-pointer hover:bg-dark-700/50'}
+                    ${isSelected ? 'bg-primary-500/10' : ''}
+                    transition-colors
+                  `}
+                >
+                  {!readOnly && (
+                    <td className="px-2 py-2">
+                      <div className={`w-5 h-5 rounded border flex items-center justify-center
+                        ${isSelected 
+                          ? 'bg-primary-500 border-primary-500' 
+                          : 'border-dark-500 bg-dark-700'
+                        }`}
+                      >
+                        {isSelected && <Check className="w-3 h-3 text-white" />}
+                      </div>
+                    </td>
+                  )}
+                  <td className="px-3 py-2">
+                    <div className="flex items-center gap-2">
+                      <code className={`text-xs ${isSelected ? 'text-primary-300' : 'text-white'}`}>
+                        {expr.name}
+                      </code>
+                      {expr.formula && (
+                        <span className="text-xs text-dark-500" title={expr.formula}>
+                          <Info className="w-3 h-3" />
+                        </span>
+                      )}
+                    </div>
+                  </td>
+                  <td className="px-3 py-2 text-right font-mono text-xs text-dark-300">
+                    {expr.value !== null ? expr.value.toFixed(3) : '-'}
+                  </td>
+                  <td className="px-3 py-2 text-xs text-dark-400">
+                    {expr.units || '-'}
+                  </td>
+                  <td className="px-3 py-2 text-center">
+                    {expr.is_candidate ? (
+                      <span className="inline-flex items-center gap-1 px-1.5 py-0.5 rounded text-xs 
+                                       bg-green-500/10 text-green-400">
+                        <Sparkles className="w-3 h-3" />
+                        {Math.round(expr.confidence * 100)}%
+                      </span>
+                    ) : (
+                      <span className="text-xs text-dark-500">-</span>
+                    )}
+                  </td>
+                </tr>
+              );
+            })}
+          </tbody>
+        </table>
+        
+        {sortedExpressions.length === 0 && (
+          <div className="px-4 py-8 text-center text-dark-500">
+            No expressions match your filter
+          </div>
+        )}
+      </div>
+
+      {/* Help text */}
+      {!readOnly && !compact && (
+        <p className="text-xs text-dark-500">
+          Select expressions to use as design variables. Candidates (marked with %) are 
+          automatically identified based on naming patterns and units.
+        </p>
+      )}
+    </div>
+  );
+};
+
+export default ExpressionList;

atomizer-dashboard/frontend/src/components/intake/FileDropzone.tsx

@@ -0,0 +1,348 @@
+/**
+ * FileDropzone - Drag and drop file upload component
+ *
+ * Supports drag-and-drop or click-to-browse for model files.
+ * Accepts .prt, .sim, .fem, .afem files.
+ */
+
+import React, { useState, useCallback, useRef } from 'react';
+import { Upload, FileText, X, Loader2, AlertCircle, CheckCircle } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface FileDropzoneProps {
+  studyName: string;
+  onUploadComplete: () => void;
+  compact?: boolean;
+}
+
+interface FileStatus {
+  file: File;
+  status: 'pending' | 'uploading' | 'success' | 'error';
+  message?: string;
+}
+
+const VALID_EXTENSIONS = ['.prt', '.sim', '.fem', '.afem'];
+
+export const FileDropzone: React.FC<FileDropzoneProps> = ({
+  studyName,
+  onUploadComplete,
+  compact = false,
+}) => {
+  const [isDragging, setIsDragging] = useState(false);
+  const [files, setFiles] = useState<FileStatus[]>([]);
+  const [isUploading, setIsUploading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+  const validateFile = (file: File): { valid: boolean; reason?: string } => {
+    const ext = '.' + file.name.split('.').pop()?.toLowerCase();
+    if (!VALID_EXTENSIONS.includes(ext)) {
+      return { valid: false, reason: `Invalid type: ${ext}` };
+    }
+    // Max 500MB per file
+    if (file.size > 500 * 1024 * 1024) {
+      return { valid: false, reason: 'File too large (max 500MB)' };
+    }
+    return { valid: true };
+  };
+
+  const handleDragEnter = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(true);
+  }, []);
+
+  const handleDragLeave = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(false);
+  }, []);
+
+  const handleDragOver = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+  }, []);
+
+  const addFiles = useCallback((newFiles: File[]) => {
+    const validFiles: FileStatus[] = [];
+    
+    for (const file of newFiles) {
+      // Skip duplicates
+      if (files.some(f => f.file.name === file.name)) {
+        continue;
+      }
+      
+      const validation = validateFile(file);
+      if (validation.valid) {
+        validFiles.push({ file, status: 'pending' });
+      } else {
+        validFiles.push({ file, status: 'error', message: validation.reason });
+      }
+    }
+    
+    setFiles(prev => [...prev, ...validFiles]);
+  }, [files]);
+
+  const handleDrop = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(false);
+    
+    const droppedFiles = Array.from(e.dataTransfer.files);
+    addFiles(droppedFiles);
+  }, [addFiles]);
+
+  const handleFileSelect = useCallback((e: React.ChangeEvent<HTMLInputElement>) => {
+    const selectedFiles = Array.from(e.target.files || []);
+    addFiles(selectedFiles);
+    // Reset input so the same file can be selected again
+    e.target.value = '';
+  }, [addFiles]);
+
+  const removeFile = (index: number) => {
+    setFiles(prev => prev.filter((_, i) => i !== index));
+  };
+
+  const handleUpload = async () => {
+    const pendingFiles = files.filter(f => f.status === 'pending');
+    if (pendingFiles.length === 0) return;
+
+    setIsUploading(true);
+    setError(null);
+
+    try {
+      // Upload files
+      const response = await intakeApi.uploadFiles(
+        studyName,
+        pendingFiles.map(f => f.file)
+      );
+
+      // Update file statuses based on response
+      const uploadResults = new Map(
+        response.uploaded_files.map(f => [f.name, f.status === 'uploaded'])
+      );
+
+      setFiles(prev => prev.map(f => {
+        if (f.status !== 'pending') return f;
+        const success = uploadResults.get(f.file.name);
+        return {
+          ...f,
+          status: success ? 'success' : 'error',
+          message: success ? undefined : 'Upload failed',
+        };
+      }));
+
+      // Clear successful uploads after a moment and refresh
+      setTimeout(() => {
+        setFiles(prev => prev.filter(f => f.status !== 'success'));
+        onUploadComplete();
+      }, 1500);
+
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Upload failed');
+      setFiles(prev => prev.map(f => 
+        f.status === 'pending' 
+          ? { ...f, status: 'error', message: 'Upload failed' }
+          : f
+      ));
+    } finally {
+      setIsUploading(false);
+    }
+  };
+
+  const pendingCount = files.filter(f => f.status === 'pending').length;
+
+  if (compact) {
+    // Compact inline version
+    return (
+      <div className="space-y-2">
+        <div className="flex items-center gap-2">
+          <button
+            onClick={() => fileInputRef.current?.click()}
+            className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                       bg-dark-700 text-dark-300 hover:bg-dark-600 hover:text-white
+                       transition-colors"
+          >
+            <Upload className="w-4 h-4" />
+            Add Files
+          </button>
+          {pendingCount > 0 && (
+            <button
+              onClick={handleUpload}
+              disabled={isUploading}
+              className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                         bg-primary-500/10 text-primary-400 hover:bg-primary-500/20
+                         disabled:opacity-50 transition-colors"
+            >
+              {isUploading ? (
+                <Loader2 className="w-4 h-4 animate-spin" />
+              ) : (
+                <Upload className="w-4 h-4" />
+              )}
+              Upload {pendingCount} {pendingCount === 1 ? 'File' : 'Files'}
+            </button>
+          )}
+        </div>
+        
+        {/* File list */}
+        {files.length > 0 && (
+          <div className="flex flex-wrap gap-2">
+            {files.map((f, i) => (
+              <span
+                key={i}
+                className={`inline-flex items-center gap-1.5 px-2 py-1 rounded text-xs
+                           ${f.status === 'error' ? 'bg-red-500/10 text-red-400' : 
+                             f.status === 'success' ? 'bg-green-500/10 text-green-400' :
+                             'bg-dark-700 text-dark-300'}`}
+              >
+                {f.status === 'uploading' && <Loader2 className="w-3 h-3 animate-spin" />}
+                {f.status === 'success' && <CheckCircle className="w-3 h-3" />}
+                {f.status === 'error' && <AlertCircle className="w-3 h-3" />}
+                {f.file.name}
+                {f.status === 'pending' && (
+                  <button onClick={() => removeFile(i)} className="hover:text-white">
+                    <X className="w-3 h-3" />
+                  </button>
+                )}
+              </span>
+            ))}
+          </div>
+        )}
+        
+        <input
+          ref={fileInputRef}
+          type="file"
+          multiple
+          accept={VALID_EXTENSIONS.join(',')}
+          onChange={handleFileSelect}
+          className="hidden"
+        />
+      </div>
+    );
+  }
+
+  // Full dropzone version
+  return (
+    <div className="space-y-4">
+      {/* Dropzone */}
+      <div
+        onDragEnter={handleDragEnter}
+        onDragLeave={handleDragLeave}
+        onDragOver={handleDragOver}
+        onDrop={handleDrop}
+        onClick={() => fileInputRef.current?.click()}
+        className={`
+          relative border-2 border-dashed rounded-xl p-6 cursor-pointer
+          transition-all duration-200
+          ${isDragging 
+            ? 'border-primary-400 bg-primary-400/5' 
+            : 'border-dark-600 hover:border-primary-400/50 hover:bg-white/5'
+          }
+        `}
+      >
+        <div className="flex flex-col items-center text-center">
+          <div className={`w-12 h-12 rounded-full flex items-center justify-center mb-3
+                          ${isDragging ? 'bg-primary-400/20 text-primary-400' : 'bg-dark-700 text-dark-400'}`}>
+            <Upload className="w-6 h-6" />
+          </div>
+          <p className="text-white font-medium mb-1">
+            {isDragging ? 'Drop files here' : 'Drop model files here'}
+          </p>
+          <p className="text-sm text-dark-400">
+            or <span className="text-primary-400">click to browse</span>
+          </p>
+          <p className="text-xs text-dark-500 mt-2">
+            Accepts: {VALID_EXTENSIONS.join(', ')}
+          </p>
+        </div>
+      </div>
+
+      {/* Error */}
+      {error && (
+        <div className="p-3 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-sm flex items-center gap-2">
+          <AlertCircle className="w-4 h-4 flex-shrink-0" />
+          {error}
+        </div>
+      )}
+
+      {/* File List */}
+      {files.length > 0 && (
+        <div className="space-y-2">
+          <h5 className="text-sm font-medium text-dark-300">Files to Upload</h5>
+          <div className="space-y-1">
+            {files.map((f, i) => (
+              <div
+                key={i}
+                className={`flex items-center justify-between p-2 rounded-lg
+                           ${f.status === 'error' ? 'bg-red-500/10' : 
+                             f.status === 'success' ? 'bg-green-500/10' :
+                             'bg-dark-700'}`}
+              >
+                <div className="flex items-center gap-2">
+                  {f.status === 'pending' && <FileText className="w-4 h-4 text-dark-400" />}
+                  {f.status === 'uploading' && <Loader2 className="w-4 h-4 text-primary-400 animate-spin" />}
+                  {f.status === 'success' && <CheckCircle className="w-4 h-4 text-green-400" />}
+                  {f.status === 'error' && <AlertCircle className="w-4 h-4 text-red-400" />}
+                  <span className={`text-sm ${f.status === 'error' ? 'text-red-400' : 
+                                              f.status === 'success' ? 'text-green-400' : 
+                                              'text-white'}`}>
+                    {f.file.name}
+                  </span>
+                  {f.message && (
+                    <span className="text-xs text-red-400">({f.message})</span>
+                  )}
+                </div>
+                {f.status === 'pending' && (
+                  <button
+                    onClick={(e) => {
+                      e.stopPropagation();
+                      removeFile(i);
+                    }}
+                    className="p-1 hover:bg-white/10 rounded text-dark-400 hover:text-white"
+                  >
+                    <X className="w-4 h-4" />
+                  </button>
+                )}
+              </div>
+            ))}
+          </div>
+
+          {/* Upload Button */}
+          {pendingCount > 0 && (
+            <button
+              onClick={handleUpload}
+              disabled={isUploading}
+              className="w-full flex items-center justify-center gap-2 px-4 py-2 rounded-lg
+                         bg-primary-500 text-white font-medium
+                         hover:bg-primary-400 disabled:opacity-50 disabled:cursor-not-allowed
+                         transition-colors"
+            >
+              {isUploading ? (
+                <>
+                  <Loader2 className="w-4 h-4 animate-spin" />
+                  Uploading...
+                </>
+              ) : (
+                <>
+                  <Upload className="w-4 h-4" />
+                  Upload {pendingCount} {pendingCount === 1 ? 'File' : 'Files'}
+                </>
+              )}
+            </button>
+          )}
+        </div>
+      )}
+
+      <input
+        ref={fileInputRef}
+        type="file"
+        multiple
+        accept={VALID_EXTENSIONS.join(',')}
+        onChange={handleFileSelect}
+        className="hidden"
+      />
+    </div>
+  );
+};
+
+export default FileDropzone;

atomizer-dashboard/frontend/src/components/intake/FinalizeModal.tsx

@@ -0,0 +1,272 @@
+/**
+ * FinalizeModal - Modal for finalizing an inbox study
+ *
+ * Allows user to:
+ * - Select/create topic folder
+ * - Choose whether to run baseline FEA
+ * - See progress during finalization
+ */
+
+import React, { useState, useEffect } from 'react';
+import {
+  X,
+  Folder,
+  CheckCircle,
+  Loader2,
+  AlertCircle,
+} from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+import { TopicInfo, InboxStudyDetail } from '../../types/intake';
+
+interface FinalizeModalProps {
+  studyName: string;
+  topics: TopicInfo[];
+  onClose: () => void;
+  onFinalized: (finalPath: string) => void;
+}
+
+export const FinalizeModal: React.FC<FinalizeModalProps> = ({
+  studyName,
+  topics,
+  onClose,
+  onFinalized,
+}) => {
+  const [studyDetail, setStudyDetail] = useState<InboxStudyDetail | null>(null);
+  const [selectedTopic, setSelectedTopic] = useState('');
+  const [newTopic, setNewTopic] = useState('');
+  const [runBaseline, setRunBaseline] = useState(true);
+  const [isLoading, setIsLoading] = useState(true);
+  const [isFinalizing, setIsFinalizing] = useState(false);
+  const [progress, setProgress] = useState<string>('');
+  const [error, setError] = useState<string | null>(null);
+
+  // Load study detail
+  useEffect(() => {
+    const loadStudy = async () => {
+      try {
+        const detail = await intakeApi.getInboxStudy(studyName);
+        setStudyDetail(detail);
+        // Pre-select topic if set in spec
+        if (detail.spec.meta.topic) {
+          setSelectedTopic(detail.spec.meta.topic);
+        }
+      } catch (err) {
+        setError(err instanceof Error ? err.message : 'Failed to load study');
+      } finally {
+        setIsLoading(false);
+      }
+    };
+    loadStudy();
+  }, [studyName]);
+
+  const handleFinalize = async () => {
+    const topic = newTopic.trim() || selectedTopic;
+    if (!topic) {
+      setError('Please select or create a topic folder');
+      return;
+    }
+
+    setIsFinalizing(true);
+    setError(null);
+    setProgress('Starting finalization...');
+
+    try {
+      setProgress('Validating study configuration...');
+      await new Promise((r) => setTimeout(r, 500)); // Visual feedback
+
+      if (runBaseline) {
+        setProgress('Running baseline FEA solve...');
+      }
+
+      const result = await intakeApi.finalize(studyName, {
+        topic,
+        run_baseline: runBaseline,
+      });
+
+      setProgress('Finalization complete!');
+      await new Promise((r) => setTimeout(r, 500));
+
+      onFinalized(result.final_path);
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Finalization failed');
+      setIsFinalizing(false);
+    }
+  };
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center bg-dark-900/80 backdrop-blur-sm">
+      <div className="w-full max-w-lg glass-strong rounded-xl border border-primary-400/20 overflow-hidden">
+        {/* Header */}
+        <div className="px-6 py-4 border-b border-primary-400/10 flex items-center justify-between">
+          <div className="flex items-center gap-3">
+            <div className="w-10 h-10 rounded-lg bg-primary-400/10 flex items-center justify-center">
+              <Folder className="w-5 h-5 text-primary-400" />
+            </div>
+            <div>
+              <h3 className="text-lg font-semibold text-white">Finalize Study</h3>
+              <p className="text-sm text-dark-400">{studyName}</p>
+            </div>
+          </div>
+          {!isFinalizing && (
+            <button
+              onClick={onClose}
+              className="p-2 hover:bg-white/5 rounded-lg transition-colors text-dark-400 hover:text-white"
+            >
+              <X className="w-5 h-5" />
+            </button>
+          )}
+        </div>
+
+        {/* Content */}
+        <div className="p-6 space-y-6">
+          {isLoading ? (
+            <div className="flex items-center justify-center py-8">
+              <Loader2 className="w-6 h-6 animate-spin text-primary-400" />
+            </div>
+          ) : isFinalizing ? (
+            /* Progress View */
+            <div className="text-center py-8 space-y-4">
+              <Loader2 className="w-12 h-12 animate-spin text-primary-400 mx-auto" />
+              <p className="text-white font-medium">{progress}</p>
+              <p className="text-sm text-dark-400">
+                Please wait while your study is being finalized...
+              </p>
+            </div>
+          ) : (
+            <>
+              {/* Error Display */}
+              {error && (
+                <div className="p-3 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-sm flex items-center gap-2">
+                  <AlertCircle className="w-4 h-4 flex-shrink-0" />
+                  {error}
+                </div>
+              )}
+
+              {/* Study Summary */}
+              {studyDetail && (
+                <div className="p-4 rounded-lg bg-dark-800 space-y-2">
+                  <h4 className="text-sm font-medium text-dark-300">Study Summary</h4>
+                  <div className="grid grid-cols-2 gap-4 text-sm">
+                    <div>
+                      <span className="text-dark-500">Status:</span>
+                      <span className="ml-2 text-white capitalize">
+                        {studyDetail.spec.meta.status}
+                      </span>
+                    </div>
+                    <div>
+                      <span className="text-dark-500">Model Files:</span>
+                      <span className="ml-2 text-white">
+                        {studyDetail.files.sim.length + studyDetail.files.prt.length + studyDetail.files.fem.length}
+                      </span>
+                    </div>
+                    <div>
+                      <span className="text-dark-500">Design Variables:</span>
+                      <span className="ml-2 text-white">
+                        {studyDetail.spec.design_variables?.length || 0}
+                      </span>
+                    </div>
+                    <div>
+                      <span className="text-dark-500">Objectives:</span>
+                      <span className="ml-2 text-white">
+                        {studyDetail.spec.objectives?.length || 0}
+                      </span>
+                    </div>
+                  </div>
+                </div>
+              )}
+
+              {/* Topic Selection */}
+              <div>
+                <label className="block text-sm font-medium text-dark-300 mb-2">
+                  Topic Folder <span className="text-red-400">*</span>
+                </label>
+                <div className="flex gap-2">
+                  <select
+                    value={selectedTopic}
+                    onChange={(e) => {
+                      setSelectedTopic(e.target.value);
+                      setNewTopic('');
+                    }}
+                    className="flex-1 px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                               text-white focus:border-primary-400 focus:outline-none 
+                               focus:ring-1 focus:ring-primary-400/50"
+                  >
+                    <option value="">Select existing topic...</option>
+                    {topics.map((topic) => (
+                      <option key={topic.name} value={topic.name}>
+                        {topic.name} ({topic.study_count} studies)
+                      </option>
+                    ))}
+                  </select>
+                  <span className="text-dark-500 self-center">or</span>
+                  <input
+                    type="text"
+                    value={newTopic}
+                    onChange={(e) => {
+                      setNewTopic(e.target.value.replace(/[^A-Za-z0-9_]/g, '_'));
+                      setSelectedTopic('');
+                    }}
+                    placeholder="New_Topic"
+                    className="flex-1 px-4 py-2.5 rounded-lg bg-dark-800 border border-dark-600 
+                               text-white placeholder-dark-500 focus:border-primary-400 
+                               focus:outline-none focus:ring-1 focus:ring-primary-400/50"
+                  />
+                </div>
+                <p className="mt-1 text-xs text-dark-500">
+                  Study will be created at: studies/{newTopic || selectedTopic || '<topic>'}/{studyName}/
+                </p>
+              </div>
+
+              {/* Baseline Option */}
+              <div>
+                <label className="flex items-center gap-3 cursor-pointer">
+                  <input
+                    type="checkbox"
+                    checked={runBaseline}
+                    onChange={(e) => setRunBaseline(e.target.checked)}
+                    className="w-4 h-4 rounded border-dark-600 bg-dark-800 text-primary-400 
+                               focus:ring-primary-400/50"
+                  />
+                  <div>
+                    <span className="text-white font-medium">Run baseline FEA solve</span>
+                    <p className="text-xs text-dark-500">
+                      Validates the model and captures baseline performance metrics
+                    </p>
+                  </div>
+                </label>
+              </div>
+            </>
+          )}
+        </div>
+
+        {/* Footer */}
+        {!isLoading && !isFinalizing && (
+          <div className="px-6 py-4 border-t border-primary-400/10 flex justify-end gap-3">
+            <button
+              onClick={onClose}
+              className="px-4 py-2 rounded-lg border border-dark-600 text-dark-300 
+                         hover:border-dark-500 hover:text-white transition-colors"
+            >
+              Cancel
+            </button>
+            <button
+              onClick={handleFinalize}
+              disabled={!selectedTopic && !newTopic.trim()}
+              className="px-6 py-2 rounded-lg font-medium transition-all disabled:opacity-50
+                         flex items-center gap-2"
+              style={{
+                background: 'linear-gradient(135deg, #00d4e6 0%, #0891b2 100%)',
+                color: '#000',
+              }}
+            >
+              <CheckCircle className="w-4 h-4" />
+              Finalize Study
+            </button>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+};
+
+export default FinalizeModal;

atomizer-dashboard/frontend/src/components/intake/InboxSection.tsx

@@ -0,0 +1,147 @@
+/**
+ * InboxSection - Section displaying inbox studies on Home page
+ *
+ * Shows the "Create New Study" card and lists all inbox studies
+ * with their current status and available actions.
+ */
+
+import React, { useState, useEffect, useCallback } from 'react';
+import { Inbox, RefreshCw, ChevronDown, ChevronRight } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+import { InboxStudy, TopicInfo } from '../../types/intake';
+import { CreateStudyCard } from './CreateStudyCard';
+import { InboxStudyCard } from './InboxStudyCard';
+import { FinalizeModal } from './FinalizeModal';
+
+interface InboxSectionProps {
+  onStudyFinalized?: () => void;
+}
+
+export const InboxSection: React.FC<InboxSectionProps> = ({ onStudyFinalized }) => {
+  const [inboxStudies, setInboxStudies] = useState<InboxStudy[]>([]);
+  const [topics, setTopics] = useState<TopicInfo[]>([]);
+  const [isLoading, setIsLoading] = useState(true);
+  const [isExpanded, setIsExpanded] = useState(true);
+  const [selectedStudyForFinalize, setSelectedStudyForFinalize] = useState<string | null>(null);
+
+  const loadData = useCallback(async () => {
+    setIsLoading(true);
+    try {
+      const [inboxResponse, topicsResponse] = await Promise.all([
+        intakeApi.listInbox(),
+        intakeApi.listTopics(),
+      ]);
+      setInboxStudies(inboxResponse.studies);
+      setTopics(topicsResponse.topics);
+    } catch (err) {
+      console.error('Failed to load inbox data:', err);
+    } finally {
+      setIsLoading(false);
+    }
+  }, []);
+
+  useEffect(() => {
+    loadData();
+  }, [loadData]);
+
+  const handleStudyCreated = (_studyName: string) => {
+    loadData();
+  };
+
+  const handleStudyFinalized = (_finalPath: string) => {
+    setSelectedStudyForFinalize(null);
+    loadData();
+    onStudyFinalized?.();
+  };
+
+  const pendingStudies = inboxStudies.filter(
+    (s) => !['ready', 'running', 'completed'].includes(s.status)
+  );
+
+  return (
+    <div className="space-y-4">
+      {/* Section Header */}
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className="w-full flex items-center justify-between px-2 py-1 hover:bg-white/5 rounded-lg transition-colors"
+      >
+        <div className="flex items-center gap-3">
+          <div className="w-8 h-8 rounded-lg bg-primary-400/10 flex items-center justify-center">
+            <Inbox className="w-4 h-4 text-primary-400" />
+          </div>
+          <div className="text-left">
+            <h2 className="text-lg font-semibold text-white">Study Inbox</h2>
+            <p className="text-sm text-dark-400">
+              {pendingStudies.length} pending studies
+            </p>
+          </div>
+        </div>
+        <div className="flex items-center gap-2">
+          <button
+            onClick={(e) => {
+              e.stopPropagation();
+              loadData();
+            }}
+            className="p-2 hover:bg-white/5 rounded-lg transition-colors text-dark-400 hover:text-primary-400"
+            title="Refresh"
+          >
+            <RefreshCw className={`w-4 h-4 ${isLoading ? 'animate-spin' : ''}`} />
+          </button>
+          {isExpanded ? (
+            <ChevronDown className="w-5 h-5 text-dark-400" />
+          ) : (
+            <ChevronRight className="w-5 h-5 text-dark-400" />
+          )}
+        </div>
+      </button>
+
+      {/* Content */}
+      {isExpanded && (
+        <div className="space-y-4">
+          {/* Create Study Card */}
+          <CreateStudyCard topics={topics} onStudyCreated={handleStudyCreated} />
+
+          {/* Inbox Studies List */}
+          {inboxStudies.length > 0 && (
+            <div className="space-y-3">
+              <h3 className="text-sm font-medium text-dark-400 px-2">
+                Inbox Studies ({inboxStudies.length})
+              </h3>
+              {inboxStudies.map((study) => (
+                <InboxStudyCard
+                  key={study.study_name}
+                  study={study}
+                  onRefresh={loadData}
+                  onSelect={setSelectedStudyForFinalize}
+                />
+              ))}
+            </div>
+          )}
+
+          {/* Empty State */}
+          {!isLoading && inboxStudies.length === 0 && (
+            <div className="text-center py-8 text-dark-400">
+              <Inbox className="w-12 h-12 mx-auto mb-3 opacity-30" />
+              <p>No studies in inbox</p>
+              <p className="text-sm text-dark-500">
+                Create a new study to get started
+              </p>
+            </div>
+          )}
+        </div>
+      )}
+
+      {/* Finalize Modal */}
+      {selectedStudyForFinalize && (
+        <FinalizeModal
+          studyName={selectedStudyForFinalize}
+          topics={topics}
+          onClose={() => setSelectedStudyForFinalize(null)}
+          onFinalized={handleStudyFinalized}
+        />
+      )}
+    </div>
+  );
+};
+
+export default InboxSection;

atomizer-dashboard/frontend/src/components/intake/InboxStudyCard.tsx

@@ -0,0 +1,455 @@
+/**
+ * InboxStudyCard - Card displaying an inbox study with actions
+ *
+ * Shows study status, files, and provides actions for:
+ * - Running introspection
+ * - Generating README
+ * - Finalizing the study
+ */
+
+import React, { useState, useEffect } from 'react';
+import {
+  FileText,
+  Folder,
+  Trash2,
+  Play,
+  CheckCircle,
+  Clock,
+  AlertCircle,
+  Loader2,
+  ChevronDown,
+  ChevronRight,
+  Sparkles,
+  ArrowRight,
+  Eye,
+  Save,
+} from 'lucide-react';
+import { InboxStudy, SpecStatus, ExpressionInfo, InboxStudyDetail } from '../../types/intake';
+import { intakeApi } from '../../api/intake';
+import { FileDropzone } from './FileDropzone';
+import { ContextFileUpload } from './ContextFileUpload';
+import { ExpressionList } from './ExpressionList';
+
+interface InboxStudyCardProps {
+  study: InboxStudy;
+  onRefresh: () => void;
+  onSelect: (studyName: string) => void;
+}
+
+const statusConfig: Record<SpecStatus, { icon: React.ReactNode; color: string; label: string }> = {
+  draft: {
+    icon: <Clock className="w-4 h-4" />,
+    color: 'text-dark-400 bg-dark-600',
+    label: 'Draft',
+  },
+  introspected: {
+    icon: <CheckCircle className="w-4 h-4" />,
+    color: 'text-blue-400 bg-blue-500/10',
+    label: 'Introspected',
+  },
+  configured: {
+    icon: <CheckCircle className="w-4 h-4" />,
+    color: 'text-green-400 bg-green-500/10',
+    label: 'Configured',
+  },
+  validated: {
+    icon: <CheckCircle className="w-4 h-4" />,
+    color: 'text-green-400 bg-green-500/10',
+    label: 'Validated',
+  },
+  ready: {
+    icon: <CheckCircle className="w-4 h-4" />,
+    color: 'text-primary-400 bg-primary-500/10',
+    label: 'Ready',
+  },
+  running: {
+    icon: <Play className="w-4 h-4" />,
+    color: 'text-yellow-400 bg-yellow-500/10',
+    label: 'Running',
+  },
+  completed: {
+    icon: <CheckCircle className="w-4 h-4" />,
+    color: 'text-green-400 bg-green-500/10',
+    label: 'Completed',
+  },
+  failed: {
+    icon: <AlertCircle className="w-4 h-4" />,
+    color: 'text-red-400 bg-red-500/10',
+    label: 'Failed',
+  },
+};
+
+export const InboxStudyCard: React.FC<InboxStudyCardProps> = ({
+  study,
+  onRefresh,
+  onSelect,
+}) => {
+  const [isExpanded, setIsExpanded] = useState(false);
+  const [isIntrospecting, setIsIntrospecting] = useState(false);
+  const [isGeneratingReadme, setIsGeneratingReadme] = useState(false);
+  const [isDeleting, setIsDeleting] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  
+  // Introspection data (fetched when expanded)
+  const [studyDetail, setStudyDetail] = useState<InboxStudyDetail | null>(null);
+  const [isLoadingDetail, setIsLoadingDetail] = useState(false);
+  const [selectedExpressions, setSelectedExpressions] = useState<string[]>([]);
+  const [showReadme, setShowReadme] = useState(false);
+  const [readmeContent, setReadmeContent] = useState<string | null>(null);
+  const [isSavingDVs, setIsSavingDVs] = useState(false);
+  const [dvSaveMessage, setDvSaveMessage] = useState<string | null>(null);
+
+  const status = statusConfig[study.status] || statusConfig.draft;
+  
+  // Fetch study details when expanded for the first time
+  useEffect(() => {
+    if (isExpanded && !studyDetail && !isLoadingDetail) {
+      loadStudyDetail();
+    }
+  }, [isExpanded]);
+  
+  const loadStudyDetail = async () => {
+    setIsLoadingDetail(true);
+    try {
+      const detail = await intakeApi.getInboxStudy(study.study_name);
+      setStudyDetail(detail);
+      
+      // Auto-select candidate expressions
+      const introspection = detail.spec?.model?.introspection;
+      if (introspection?.expressions) {
+        const candidates = introspection.expressions
+          .filter((e: ExpressionInfo) => e.is_candidate)
+          .map((e: ExpressionInfo) => e.name);
+        setSelectedExpressions(candidates);
+      }
+    } catch (err) {
+      console.error('Failed to load study detail:', err);
+    } finally {
+      setIsLoadingDetail(false);
+    }
+  };
+
+  const handleIntrospect = async () => {
+    setIsIntrospecting(true);
+    setError(null);
+    try {
+      await intakeApi.introspect({ study_name: study.study_name });
+      // Reload study detail to get new introspection data
+      await loadStudyDetail();
+      onRefresh();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Introspection failed');
+    } finally {
+      setIsIntrospecting(false);
+    }
+  };
+
+  const handleGenerateReadme = async () => {
+    setIsGeneratingReadme(true);
+    setError(null);
+    try {
+      const response = await intakeApi.generateReadme(study.study_name);
+      setReadmeContent(response.content);
+      setShowReadme(true);
+      onRefresh();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'README generation failed');
+    } finally {
+      setIsGeneratingReadme(false);
+    }
+  };
+
+  const handleDelete = async () => {
+    if (!confirm(`Delete inbox study "${study.study_name}"? This cannot be undone.`)) {
+      return;
+    }
+    setIsDeleting(true);
+    try {
+      await intakeApi.deleteInboxStudy(study.study_name);
+      onRefresh();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Delete failed');
+      setIsDeleting(false);
+    }
+  };
+
+  const handleSaveDesignVariables = async () => {
+    if (selectedExpressions.length === 0) {
+      setError('Please select at least one expression to use as a design variable');
+      return;
+    }
+    
+    setIsSavingDVs(true);
+    setError(null);
+    setDvSaveMessage(null);
+    
+    try {
+      const result = await intakeApi.createDesignVariables(study.study_name, selectedExpressions);
+      setDvSaveMessage(`Created ${result.total_created} design variable(s)`);
+      // Reload study detail to see updated spec
+      await loadStudyDetail();
+      onRefresh();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Failed to save design variables');
+    } finally {
+      setIsSavingDVs(false);
+    }
+  };
+
+  const canIntrospect = study.status === 'draft' && study.model_files.length > 0;
+  const canGenerateReadme = study.status === 'introspected';
+  const canFinalize = ['introspected', 'configured'].includes(study.status);
+  const canSaveDVs = study.status === 'introspected' && selectedExpressions.length > 0;
+
+  return (
+    <div className="glass rounded-xl border border-primary-400/10 overflow-hidden">
+      {/* Header - Always visible */}
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className="w-full px-4 py-3 flex items-center justify-between hover:bg-white/5 transition-colors"
+      >
+        <div className="flex items-center gap-3">
+          <div className="w-10 h-10 rounded-lg bg-dark-700 flex items-center justify-center">
+            <Folder className="w-5 h-5 text-primary-400" />
+          </div>
+          <div className="text-left">
+            <h4 className="text-white font-medium">{study.study_name}</h4>
+            {study.description && (
+              <p className="text-sm text-dark-400 truncate max-w-[300px]">
+                {study.description}
+              </p>
+            )}
+          </div>
+        </div>
+        <div className="flex items-center gap-3">
+          {/* Status Badge */}
+          <span className={`inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-xs font-medium ${status.color}`}>
+            {status.icon}
+            {status.label}
+          </span>
+          {/* File Count */}
+          <span className="text-dark-500 text-sm">
+            {study.model_files.length} files
+          </span>
+          {/* Expand Icon */}
+          {isExpanded ? (
+            <ChevronDown className="w-4 h-4 text-dark-400" />
+          ) : (
+            <ChevronRight className="w-4 h-4 text-dark-400" />
+          )}
+        </div>
+      </button>
+
+      {/* Expanded Content */}
+      {isExpanded && (
+        <div className="px-4 pb-4 space-y-4 border-t border-primary-400/10 pt-4">
+          {/* Error Display */}
+          {error && (
+            <div className="p-3 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-sm flex items-center gap-2">
+              <AlertCircle className="w-4 h-4 flex-shrink-0" />
+              {error}
+            </div>
+          )}
+
+          {/* Success Message */}
+          {dvSaveMessage && (
+            <div className="p-3 rounded-lg bg-green-500/10 border border-green-500/30 text-green-400 text-sm flex items-center gap-2">
+              <CheckCircle className="w-4 h-4 flex-shrink-0" />
+              {dvSaveMessage}
+            </div>
+          )}
+
+          {/* Files Section */}
+          {study.model_files.length > 0 && (
+            <div>
+              <h5 className="text-sm font-medium text-dark-300 mb-2">Model Files</h5>
+              <div className="flex flex-wrap gap-2">
+                {study.model_files.map((file) => (
+                  <span
+                    key={file}
+                    className="inline-flex items-center gap-1.5 px-2 py-1 rounded bg-dark-700 text-dark-300 text-xs"
+                  >
+                    <FileText className="w-3 h-3" />
+                    {file}
+                  </span>
+                ))}
+              </div>
+            </div>
+          )}
+
+          {/* Model File Upload Section */}
+          <div>
+            <h5 className="text-sm font-medium text-dark-300 mb-2">Upload Model Files</h5>
+            <FileDropzone 
+              studyName={study.study_name} 
+              onUploadComplete={onRefresh}
+              compact={true}
+            />
+          </div>
+
+          {/* Context File Upload Section */}
+          <ContextFileUpload 
+            studyName={study.study_name} 
+            onUploadComplete={onRefresh}
+          />
+
+          {/* Introspection Results - Expressions */}
+          {isLoadingDetail && (
+            <div className="flex items-center gap-2 text-dark-400 text-sm py-4">
+              <Loader2 className="w-4 h-4 animate-spin" />
+              Loading introspection data...
+            </div>
+          )}
+          
+          {studyDetail?.spec?.model?.introspection?.expressions && 
+           studyDetail.spec.model.introspection.expressions.length > 0 && (
+            <ExpressionList
+              expressions={studyDetail.spec.model.introspection.expressions}
+              massKg={studyDetail.spec.model.introspection.mass_kg}
+              selectedExpressions={selectedExpressions}
+              onSelectionChange={setSelectedExpressions}
+              readOnly={study.status === 'configured'}
+              compact={true}
+            />
+          )}
+
+          {/* README Preview Section */}
+          {(readmeContent || study.status === 'configured') && (
+            <div className="space-y-2">
+              <div className="flex items-center justify-between">
+                <h5 className="text-sm font-medium text-dark-300 flex items-center gap-2">
+                  <FileText className="w-4 h-4" />
+                  README.md
+                </h5>
+                <button
+                  onClick={() => setShowReadme(!showReadme)}
+                  className="flex items-center gap-1 px-2 py-1 text-xs rounded bg-dark-600 
+                             text-dark-300 hover:bg-dark-500 transition-colors"
+                >
+                  <Eye className="w-3 h-3" />
+                  {showReadme ? 'Hide' : 'Preview'}
+                </button>
+              </div>
+              {showReadme && readmeContent && (
+                <div className="max-h-64 overflow-y-auto rounded-lg border border-dark-600 
+                                bg-dark-800 p-4">
+                  <pre className="text-xs text-dark-300 whitespace-pre-wrap font-mono">
+                    {readmeContent}
+                  </pre>
+                </div>
+              )}
+            </div>
+          )}
+
+          {/* No Files Warning */}
+          {study.model_files.length === 0 && (
+            <div className="p-3 rounded-lg bg-yellow-500/10 border border-yellow-500/30 text-yellow-400 text-sm flex items-center gap-2">
+              <AlertCircle className="w-4 h-4 flex-shrink-0" />
+              No model files found. Upload .prt, .sim, or .fem files to continue.
+            </div>
+          )}
+
+          {/* Actions */}
+          <div className="flex flex-wrap gap-2">
+            {/* Introspect */}
+            {canIntrospect && (
+              <button
+                onClick={handleIntrospect}
+                disabled={isIntrospecting}
+                className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                           bg-blue-500/10 text-blue-400 hover:bg-blue-500/20 
+                           disabled:opacity-50 transition-colors"
+              >
+                {isIntrospecting ? (
+                  <Loader2 className="w-4 h-4 animate-spin" />
+                ) : (
+                  <Play className="w-4 h-4" />
+                )}
+                Introspect Model
+              </button>
+            )}
+
+            {/* Save Design Variables */}
+            {canSaveDVs && (
+              <button
+                onClick={handleSaveDesignVariables}
+                disabled={isSavingDVs}
+                className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                           bg-green-500/10 text-green-400 hover:bg-green-500/20 
+                           disabled:opacity-50 transition-colors"
+              >
+                {isSavingDVs ? (
+                  <Loader2 className="w-4 h-4 animate-spin" />
+                ) : (
+                  <Save className="w-4 h-4" />
+                )}
+                Save as DVs ({selectedExpressions.length})
+              </button>
+            )}
+
+            {/* Generate README */}
+            {canGenerateReadme && (
+              <button
+                onClick={handleGenerateReadme}
+                disabled={isGeneratingReadme}
+                className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                           bg-purple-500/10 text-purple-400 hover:bg-purple-500/20 
+                           disabled:opacity-50 transition-colors"
+              >
+                {isGeneratingReadme ? (
+                  <Loader2 className="w-4 h-4 animate-spin" />
+                ) : (
+                  <Sparkles className="w-4 h-4" />
+                )}
+                Generate README
+              </button>
+            )}
+
+            {/* Finalize */}
+            {canFinalize && (
+              <button
+                onClick={() => onSelect(study.study_name)}
+                className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                           bg-primary-500/10 text-primary-400 hover:bg-primary-500/20 
+                           transition-colors"
+              >
+                <ArrowRight className="w-4 h-4" />
+                Finalize Study
+              </button>
+            )}
+
+            {/* Delete */}
+            <button
+              onClick={handleDelete}
+              disabled={isDeleting}
+              className="flex items-center gap-2 px-3 py-1.5 rounded-lg text-sm font-medium
+                         bg-red-500/10 text-red-400 hover:bg-red-500/20 
+                         disabled:opacity-50 transition-colors ml-auto"
+            >
+              {isDeleting ? (
+                <Loader2 className="w-4 h-4 animate-spin" />
+              ) : (
+                <Trash2 className="w-4 h-4" />
+              )}
+              Delete
+            </button>
+          </div>
+
+          {/* Workflow Hint */}
+          {study.status === 'draft' && study.model_files.length > 0 && (
+            <p className="text-xs text-dark-500">
+              Next step: Run introspection to discover expressions and model properties.
+            </p>
+          )}
+          {study.status === 'introspected' && (
+            <p className="text-xs text-dark-500">
+              Next step: Generate README with Claude AI, then finalize to create the study.
+            </p>
+          )}
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default InboxStudyCard;

atomizer-dashboard/frontend/src/components/intake/index.ts

@@ -0,0 +1,13 @@
+/**
+ * Intake Components Index
+ *
+ * Export all intake workflow components.
+ */
+
+export { CreateStudyCard } from './CreateStudyCard';
+export { InboxStudyCard } from './InboxStudyCard';
+export { FinalizeModal } from './FinalizeModal';
+export { InboxSection } from './InboxSection';
+export { FileDropzone } from './FileDropzone';
+export { ContextFileUpload } from './ContextFileUpload';
+export { ExpressionList } from './ExpressionList';

atomizer-dashboard/frontend/src/components/studio/StudioBuildDialog.tsx

@@ -0,0 +1,254 @@
+/**
+ * StudioBuildDialog - Final dialog to name and build the study
+ */
+
+import React, { useState, useEffect } from 'react';
+import { X, Loader2, FolderOpen, AlertCircle, CheckCircle, Sparkles, Play } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface StudioBuildDialogProps {
+  draftId: string;
+  onClose: () => void;
+  onBuildComplete: (finalPath: string, finalName: string) => void;
+}
+
+interface Topic {
+  name: string;
+  study_count: number;
+}
+
+export const StudioBuildDialog: React.FC<StudioBuildDialogProps> = ({
+  draftId,
+  onClose,
+  onBuildComplete,
+}) => {
+  const [studyName, setStudyName] = useState('');
+  const [topic, setTopic] = useState('');
+  const [newTopic, setNewTopic] = useState('');
+  const [useNewTopic, setUseNewTopic] = useState(false);
+  const [topics, setTopics] = useState<Topic[]>([]);
+  const [isBuilding, setIsBuilding] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [validationErrors, setValidationErrors] = useState<string[]>([]);
+  
+  // Load topics
+  useEffect(() => {
+    loadTopics();
+  }, []);
+  
+  const loadTopics = async () => {
+    try {
+      const response = await intakeApi.listTopics();
+      setTopics(response.topics);
+      if (response.topics.length > 0) {
+        setTopic(response.topics[0].name);
+      }
+    } catch (err) {
+      console.error('Failed to load topics:', err);
+    }
+  };
+  
+  // Validate study name
+  useEffect(() => {
+    const errors: string[] = [];
+    
+    if (studyName.length > 0) {
+      if (studyName.length < 3) {
+        errors.push('Name must be at least 3 characters');
+      }
+      if (!/^[a-z0-9_]+$/.test(studyName)) {
+        errors.push('Use only lowercase letters, numbers, and underscores');
+      }
+      if (studyName.startsWith('draft_')) {
+        errors.push('Name cannot start with "draft_"');
+      }
+    }
+    
+    setValidationErrors(errors);
+  }, [studyName]);
+  
+  const handleBuild = async () => {
+    const finalTopic = useNewTopic ? newTopic : topic;
+    
+    if (!studyName || !finalTopic) {
+      setError('Please provide both a study name and topic');
+      return;
+    }
+    
+    if (validationErrors.length > 0) {
+      setError('Please fix validation errors');
+      return;
+    }
+    
+    setIsBuilding(true);
+    setError(null);
+    
+    try {
+      const response = await intakeApi.finalizeStudio(draftId, {
+        topic: finalTopic,
+        newName: studyName,
+        runBaseline: false,
+      });
+      
+      onBuildComplete(response.final_path, response.final_name);
+      
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Build failed');
+    } finally {
+      setIsBuilding(false);
+    }
+  };
+  
+  const isValid = studyName.length >= 3 && 
+                  validationErrors.length === 0 && 
+                  (topic || (useNewTopic && newTopic));
+  
+  return (
+    <div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
+      <div className="bg-dark-850 border border-dark-700 rounded-xl shadow-xl w-full max-w-lg mx-4">
+        {/* Header */}
+        <div className="flex items-center justify-between p-4 border-b border-dark-700">
+          <div className="flex items-center gap-2">
+            <Sparkles className="w-5 h-5 text-primary-400" />
+            <h2 className="text-lg font-semibold text-white">Build Study</h2>
+          </div>
+          <button
+            onClick={onClose}
+            className="p-1 hover:bg-dark-700 rounded text-dark-400 hover:text-white transition-colors"
+          >
+            <X className="w-5 h-5" />
+          </button>
+        </div>
+        
+        {/* Content */}
+        <div className="p-6 space-y-6">
+          {/* Study Name */}
+          <div>
+            <label className="block text-sm font-medium text-dark-300 mb-2">
+              Study Name
+            </label>
+            <input
+              type="text"
+              value={studyName}
+              onChange={(e) => setStudyName(e.target.value.toLowerCase().replace(/[^a-z0-9_]/g, '_'))}
+              placeholder="my_optimization_study"
+              className="w-full bg-dark-700 border border-dark-600 rounded-lg px-3 py-2 text-white placeholder-dark-500 focus:outline-none focus:border-primary-400"
+            />
+            {validationErrors.length > 0 && (
+              <div className="mt-2 space-y-1">
+                {validationErrors.map((err, i) => (
+                  <p key={i} className="text-xs text-red-400 flex items-center gap-1">
+                    <AlertCircle className="w-3 h-3" />
+                    {err}
+                  </p>
+                ))}
+              </div>
+            )}
+            {studyName.length >= 3 && validationErrors.length === 0 && (
+              <p className="mt-2 text-xs text-green-400 flex items-center gap-1">
+                <CheckCircle className="w-3 h-3" />
+                Name is valid
+              </p>
+            )}
+          </div>
+          
+          {/* Topic Selection */}
+          <div>
+            <label className="block text-sm font-medium text-dark-300 mb-2">
+              Topic Folder
+            </label>
+            
+            {!useNewTopic && topics.length > 0 && (
+              <div className="space-y-2">
+                <select
+                  value={topic}
+                  onChange={(e) => setTopic(e.target.value)}
+                  className="w-full bg-dark-700 border border-dark-600 rounded-lg px-3 py-2 text-white focus:outline-none focus:border-primary-400"
+                >
+                  {topics.map((t) => (
+                    <option key={t.name} value={t.name}>
+                      {t.name} ({t.study_count} studies)
+                    </option>
+                  ))}
+                </select>
+                <button
+                  onClick={() => setUseNewTopic(true)}
+                  className="text-sm text-primary-400 hover:text-primary-300"
+                >
+                  + Create new topic
+                </button>
+              </div>
+            )}
+            
+            {(useNewTopic || topics.length === 0) && (
+              <div className="space-y-2">
+                <input
+                  type="text"
+                  value={newTopic}
+                  onChange={(e) => setNewTopic(e.target.value.replace(/[^A-Za-z0-9_]/g, '_'))}
+                  placeholder="NewTopic"
+                  className="w-full bg-dark-700 border border-dark-600 rounded-lg px-3 py-2 text-white placeholder-dark-500 focus:outline-none focus:border-primary-400"
+                />
+                {topics.length > 0 && (
+                  <button
+                    onClick={() => setUseNewTopic(false)}
+                    className="text-sm text-dark-400 hover:text-white"
+                  >
+                    Use existing topic
+                  </button>
+                )}
+              </div>
+            )}
+          </div>
+          
+          {/* Preview */}
+          <div className="p-3 bg-dark-700/50 rounded-lg">
+            <p className="text-xs text-dark-400 mb-1">Study will be created at:</p>
+            <p className="text-sm text-white font-mono flex items-center gap-2">
+              <FolderOpen className="w-4 h-4 text-primary-400" />
+              studies/{useNewTopic ? newTopic || '...' : topic}/{studyName || '...'}
+            </p>
+          </div>
+          
+          {/* Error */}
+          {error && (
+            <div className="p-3 rounded-lg bg-red-500/10 border border-red-500/30 text-red-400 text-sm flex items-center gap-2">
+              <AlertCircle className="w-4 h-4 flex-shrink-0" />
+              {error}
+            </div>
+          )}
+        </div>
+        
+        {/* Footer */}
+        <div className="flex items-center justify-end gap-3 p-4 border-t border-dark-700">
+          <button
+            onClick={onClose}
+            disabled={isBuilding}
+            className="px-4 py-2 text-sm text-dark-300 hover:text-white hover:bg-dark-700 rounded-lg transition-colors"
+          >
+            Cancel
+          </button>
+          <button
+            onClick={handleBuild}
+            disabled={!isValid || isBuilding}
+            className="flex items-center gap-2 px-4 py-2 text-sm font-medium bg-primary-500 text-white rounded-lg hover:bg-primary-400 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
+          >
+            {isBuilding ? (
+              <>
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Building...
+              </>
+            ) : (
+              <>
+                <Play className="w-4 h-4" />
+                Build Study
+              </>
+            )}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+};
+
+export default StudioBuildDialog;

atomizer-dashboard/frontend/src/components/studio/StudioChat.tsx

@@ -0,0 +1,375 @@
+/**
+ * StudioChat - Context-aware AI chat for Studio
+ * 
+ * Uses the existing useChat hook to communicate with Claude via WebSocket.
+ * Injects model files and context documents into the conversation.
+ */
+
+import React, { useRef, useEffect, useState, useMemo } from 'react';
+import { Send, Loader2, Sparkles, FileText, Wifi, WifiOff, Bot, User, File, AlertCircle } from 'lucide-react';
+import { useChat } from '../../hooks/useChat';
+import { useSpecStore, useSpec } from '../../hooks/useSpecStore';
+import { MarkdownRenderer } from '../MarkdownRenderer';
+import { ToolCallCard } from '../chat/ToolCallCard';
+
+interface StudioChatProps {
+  draftId: string;
+  contextFiles: string[];
+  contextContent: string;
+  modelFiles: string[];
+  onSpecUpdated: () => void;
+}
+
+export const StudioChat: React.FC<StudioChatProps> = ({
+  draftId,
+  contextFiles,
+  contextContent,
+  modelFiles,
+  onSpecUpdated,
+}) => {
+  const messagesEndRef = useRef<HTMLDivElement>(null);
+  const inputRef = useRef<HTMLTextAreaElement>(null);
+  const [input, setInput] = useState('');
+  const [hasInjectedContext, setHasInjectedContext] = useState(false);
+  
+  // Get spec store for canvas updates
+  const spec = useSpec();
+  const { reloadSpec, setSpecFromWebSocket } = useSpecStore();
+  
+  // Build canvas state with full context for Claude
+  const canvasState = useMemo(() => ({
+    nodes: [],
+    edges: [],
+    studyName: draftId,
+    studyPath: `_inbox/${draftId}`,
+    // Include file info for Claude context
+    modelFiles,
+    contextFiles,
+    contextContent: contextContent.substring(0, 50000), // Limit context size
+  }), [draftId, modelFiles, contextFiles, contextContent]);
+  
+  // Use the chat hook with WebSocket
+  // Power mode gives Claude write permissions to modify the spec
+  const {
+    messages,
+    isThinking,
+    error,
+    isConnected,
+    sendMessage,
+    updateCanvasState,
+  } = useChat({
+    studyId: draftId,
+    mode: 'power',  // Power mode = --dangerously-skip-permissions = can write files
+    useWebSocket: true,
+    canvasState,
+    onError: (err) => console.error('[StudioChat] Error:', err),
+    onSpecUpdated: (newSpec) => {
+      // Claude modified the spec - update the store directly
+      console.log('[StudioChat] Spec updated by Claude');
+      setSpecFromWebSocket(newSpec, draftId);
+      onSpecUpdated();
+    },
+    onCanvasModification: (modification) => {
+      // Claude wants to modify canvas - reload the spec
+      console.log('[StudioChat] Canvas modification:', modification);
+      reloadSpec();
+      onSpecUpdated();
+    },
+  });
+  
+  // Update canvas state when context changes
+  useEffect(() => {
+    updateCanvasState(canvasState);
+  }, [canvasState, updateCanvasState]);
+  
+  // Scroll to bottom when messages change
+  useEffect(() => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  }, [messages]);
+  
+  // Auto-focus input
+  useEffect(() => {
+    inputRef.current?.focus();
+  }, []);
+  
+  // Build context summary for display
+  const contextSummary = useMemo(() => {
+    const parts: string[] = [];
+    if (modelFiles.length > 0) {
+      parts.push(`${modelFiles.length} model file${modelFiles.length > 1 ? 's' : ''}`);
+    }
+    if (contextFiles.length > 0) {
+      parts.push(`${contextFiles.length} context doc${contextFiles.length > 1 ? 's' : ''}`);
+    }
+    if (contextContent) {
+      parts.push(`${contextContent.length.toLocaleString()} chars context`);
+    }
+    return parts.join(', ');
+  }, [modelFiles, contextFiles, contextContent]);
+  
+  const handleSend = () => {
+    if (!input.trim() || isThinking) return;
+    
+    let messageToSend = input.trim();
+    
+    // On first message, inject full context so Claude has everything it needs
+    if (!hasInjectedContext && (modelFiles.length > 0 || contextContent)) {
+      const contextParts: string[] = [];
+      
+      // Add model files info
+      if (modelFiles.length > 0) {
+        contextParts.push(`**Model Files Uploaded:**\n${modelFiles.map(f => `- ${f}`).join('\n')}`);
+      }
+      
+      // Add context document content (full text)
+      if (contextContent) {
+        contextParts.push(`**Context Documents Content:**\n\`\`\`\n${contextContent.substring(0, 30000)}\n\`\`\``);
+      }
+      
+      // Add current spec state
+      if (spec) {
+        const dvCount = spec.design_variables?.length || 0;
+        const objCount = spec.objectives?.length || 0;
+        const extCount = spec.extractors?.length || 0;
+        if (dvCount > 0 || objCount > 0 || extCount > 0) {
+          contextParts.push(`**Current Configuration:** ${dvCount} design variables, ${objCount} objectives, ${extCount} extractors`);
+        }
+      }
+      
+      if (contextParts.length > 0) {
+        messageToSend = `${contextParts.join('\n\n')}\n\n---\n\n**User Request:** ${messageToSend}`;
+      }
+      
+      setHasInjectedContext(true);
+    }
+    
+    sendMessage(messageToSend);
+    setInput('');
+  };
+  
+  const handleKeyDown = (e: React.KeyboardEvent) => {
+    if (e.key === 'Enter' && !e.shiftKey) {
+      e.preventDefault();
+      handleSend();
+    }
+  };
+  
+  // Welcome message for empty state
+  const showWelcome = messages.length === 0;
+  
+  // Check if we have any context
+  const hasContext = modelFiles.length > 0 || contextContent.length > 0;
+  
+  return (
+    <div className="h-full flex flex-col">
+      {/* Header */}
+      <div className="p-3 border-b border-dark-700 flex-shrink-0">
+        <div className="flex items-center justify-between mb-2">
+          <div className="flex items-center gap-2">
+            <Sparkles className="w-5 h-5 text-primary-400" />
+            <span className="font-medium text-white">Studio Assistant</span>
+          </div>
+          <span className={`flex items-center gap-1 text-xs px-2 py-0.5 rounded ${
+            isConnected 
+              ? 'text-green-400 bg-green-400/10' 
+              : 'text-red-400 bg-red-400/10'
+          }`}>
+            {isConnected ? <Wifi className="w-3 h-3" /> : <WifiOff className="w-3 h-3" />}
+            {isConnected ? 'Connected' : 'Disconnected'}
+          </span>
+        </div>
+        
+        {/* Context indicator */}
+        {contextSummary && (
+          <div className="flex items-center gap-2 text-xs">
+            <div className="flex items-center gap-1 text-amber-400 bg-amber-400/10 px-2 py-1 rounded">
+              <FileText className="w-3 h-3" />
+              <span>{contextSummary}</span>
+            </div>
+            {hasContext && !hasInjectedContext && (
+              <span className="text-dark-500">Will be sent with first message</span>
+            )}
+            {hasInjectedContext && (
+              <span className="text-green-500">Context sent</span>
+            )}
+          </div>
+        )}
+      </div>
+      
+      {/* Messages */}
+      <div className="flex-1 overflow-y-auto p-3 space-y-4">
+        {/* Welcome message with context awareness */}
+        {showWelcome && (
+          <div className="flex gap-3">
+            <div className="flex-shrink-0 w-8 h-8 rounded-lg flex items-center justify-center bg-primary-500/20 text-primary-400">
+              <Bot className="w-4 h-4" />
+            </div>
+            <div className="flex-1 bg-dark-700 rounded-lg px-4 py-3 text-sm text-dark-100">
+              <MarkdownRenderer content={hasContext 
+                ? `I can see you've uploaded files. Here's what I have access to:
+
+${modelFiles.length > 0 ? `**Model Files:** ${modelFiles.join(', ')}` : ''}
+${contextContent ? `\n**Context Document:** ${contextContent.substring(0, 200)}...` : ''}
+
+Tell me what you want to optimize and I'll help you configure the study!`
+                : `Welcome to Atomizer Studio! I'm here to help you configure your optimization study.
+
+**What I can do:**
+- Read your uploaded context documents
+- Help set up design variables, objectives, and constraints
+- Create extractors for physics outputs
+- Suggest optimization strategies
+
+Upload your model files and any requirements documents, then tell me what you want to optimize!`} />
+            </div>
+          </div>
+        )}
+        
+        {/* File context display (only if we have files but no messages yet) */}
+        {showWelcome && modelFiles.length > 0 && (
+          <div className="bg-dark-800/50 rounded-lg p-3 border border-dark-700">
+            <p className="text-xs text-dark-400 mb-2 font-medium">Loaded Files:</p>
+            <div className="flex flex-wrap gap-2">
+              {modelFiles.map((file, idx) => (
+                <span key={idx} className="flex items-center gap-1 text-xs bg-blue-500/10 text-blue-400 px-2 py-1 rounded">
+                  <File className="w-3 h-3" />
+                  {file}
+                </span>
+              ))}
+              {contextFiles.map((file, idx) => (
+                <span key={idx} className="flex items-center gap-1 text-xs bg-amber-500/10 text-amber-400 px-2 py-1 rounded">
+                  <FileText className="w-3 h-3" />
+                  {file}
+                </span>
+              ))}
+            </div>
+          </div>
+        )}
+        
+        {/* Chat messages */}
+        {messages.map((msg) => {
+          const isAssistant = msg.role === 'assistant';
+          const isSystem = msg.role === 'system';
+          
+          // System messages
+          if (isSystem) {
+            return (
+              <div key={msg.id} className="flex justify-center my-2">
+                <div className="px-3 py-1 bg-dark-700/50 rounded-full text-xs text-dark-400 border border-dark-600">
+                  {msg.content}
+                </div>
+              </div>
+            );
+          }
+          
+          return (
+            <div
+              key={msg.id}
+              className={`flex gap-3 ${isAssistant ? '' : 'flex-row-reverse'}`}
+            >
+              {/* Avatar */}
+              <div
+                className={`flex-shrink-0 w-8 h-8 rounded-lg flex items-center justify-center ${
+                  isAssistant
+                    ? 'bg-primary-500/20 text-primary-400'
+                    : 'bg-dark-600 text-dark-300'
+                }`}
+              >
+                {isAssistant ? <Bot className="w-4 h-4" /> : <User className="w-4 h-4" />}
+              </div>
+              
+              {/* Message content */}
+              <div
+                className={`flex-1 max-w-[85%] rounded-lg px-4 py-3 text-sm ${
+                  isAssistant
+                    ? 'bg-dark-700 text-dark-100'
+                    : 'bg-primary-500 text-white ml-auto'
+                }`}
+              >
+                {isAssistant ? (
+                  <>
+                    {msg.content && <MarkdownRenderer content={msg.content} />}
+                    {msg.isStreaming && !msg.content && (
+                      <span className="text-dark-400">Thinking...</span>
+                    )}
+                    {/* Tool calls */}
+                    {msg.toolCalls && msg.toolCalls.length > 0 && (
+                      <div className="mt-3 space-y-2">
+                        {msg.toolCalls.map((tool, idx) => (
+                          <ToolCallCard key={idx} toolCall={tool} />
+                        ))}
+                      </div>
+                    )}
+                  </>
+                ) : (
+                  <span className="whitespace-pre-wrap">{msg.content}</span>
+                )}
+              </div>
+            </div>
+          );
+        })}
+        
+        {/* Thinking indicator */}
+        {isThinking && messages.length > 0 && !messages[messages.length - 1]?.isStreaming && (
+          <div className="flex gap-3">
+            <div className="flex-shrink-0 w-8 h-8 rounded-lg flex items-center justify-center bg-primary-500/20 text-primary-400">
+              <Bot className="w-4 h-4" />
+            </div>
+            <div className="bg-dark-700 rounded-lg px-4 py-3 flex items-center gap-2">
+              <Loader2 className="w-4 h-4 text-primary-400 animate-spin" />
+              <span className="text-sm text-dark-300">Thinking...</span>
+            </div>
+          </div>
+        )}
+        
+        {/* Error display */}
+        {error && (
+          <div className="flex gap-3">
+            <div className="flex-shrink-0 w-8 h-8 rounded-lg flex items-center justify-center bg-red-500/20 text-red-400">
+              <AlertCircle className="w-4 h-4" />
+            </div>
+            <div className="flex-1 px-4 py-3 bg-red-500/10 rounded-lg text-sm text-red-400 border border-red-500/30">
+              {error}
+            </div>
+          </div>
+        )}
+        
+        <div ref={messagesEndRef} />
+      </div>
+      
+      {/* Input */}
+      <div className="p-3 border-t border-dark-700 flex-shrink-0">
+        <div className="flex gap-2">
+          <textarea
+            ref={inputRef}
+            value={input}
+            onChange={(e) => setInput(e.target.value)}
+            onKeyDown={handleKeyDown}
+            placeholder={isConnected ? "Ask about your optimization..." : "Connecting..."}
+            disabled={!isConnected}
+            rows={1}
+            className="flex-1 bg-dark-700 border border-dark-600 rounded-lg px-3 py-2 text-sm text-white placeholder-dark-400 resize-none focus:outline-none focus:border-primary-400 disabled:opacity-50"
+          />
+          <button
+            onClick={handleSend}
+            disabled={!input.trim() || isThinking || !isConnected}
+            className="p-2 bg-primary-500 text-white rounded-lg hover:bg-primary-400 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
+          >
+            {isThinking ? (
+              <Loader2 className="w-5 h-5 animate-spin" />
+            ) : (
+              <Send className="w-5 h-5" />
+            )}
+          </button>
+        </div>
+        {!isConnected && (
+          <p className="text-xs text-dark-500 mt-1">
+            Waiting for connection to Claude...
+          </p>
+        )}
+      </div>
+    </div>
+  );
+};
+
+export default StudioChat;

atomizer-dashboard/frontend/src/components/studio/StudioContextFiles.tsx

@@ -0,0 +1,117 @@
+/**
+ * StudioContextFiles - Context document upload and display
+ */
+
+import React, { useState, useRef } from 'react';
+import { FileText, Upload, Trash2, Loader2 } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface StudioContextFilesProps {
+  draftId: string;
+  files: string[];
+  onUploadComplete: () => void;
+}
+
+export const StudioContextFiles: React.FC<StudioContextFilesProps> = ({
+  draftId,
+  files,
+  onUploadComplete,
+}) => {
+  const [isUploading, setIsUploading] = useState(false);
+  const [deleting, setDeleting] = useState<string | null>(null);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+  
+  const VALID_EXTENSIONS = ['.md', '.txt', '.pdf', '.json', '.csv', '.docx'];
+  
+  const handleFileSelect = async (e: React.ChangeEvent<HTMLInputElement>) => {
+    const selectedFiles = Array.from(e.target.files || []);
+    if (selectedFiles.length === 0) return;
+    
+    e.target.value = '';
+    setIsUploading(true);
+    
+    try {
+      await intakeApi.uploadContextFiles(draftId, selectedFiles);
+      onUploadComplete();
+    } catch (err) {
+      console.error('Failed to upload context files:', err);
+    } finally {
+      setIsUploading(false);
+    }
+  };
+  
+  const deleteFile = async (filename: string) => {
+    setDeleting(filename);
+    
+    try {
+      await intakeApi.deleteContextFile(draftId, filename);
+      onUploadComplete();
+    } catch (err) {
+      console.error('Failed to delete context file:', err);
+    } finally {
+      setDeleting(null);
+    }
+  };
+  
+  const getFileIcon = (_filename: string) => {
+    return <FileText className="w-3.5 h-3.5 text-amber-400" />;
+  };
+  
+  return (
+    <div className="space-y-2">
+      {/* File List */}
+      {files.length > 0 && (
+        <div className="space-y-1">
+          {files.map((name) => (
+            <div
+              key={name}
+              className="flex items-center gap-2 px-2 py-1.5 rounded bg-dark-700/50 text-sm group"
+            >
+              {getFileIcon(name)}
+              <span className="text-dark-200 truncate flex-1">{name}</span>
+              <button
+                onClick={() => deleteFile(name)}
+                disabled={deleting === name}
+                className="p-1 opacity-0 group-hover:opacity-100 hover:bg-red-500/20 rounded text-red-400 transition-all"
+              >
+                {deleting === name ? (
+                  <Loader2 className="w-3 h-3 animate-spin" />
+                ) : (
+                  <Trash2 className="w-3 h-3" />
+                )}
+              </button>
+            </div>
+          ))}
+        </div>
+      )}
+      
+      {/* Upload Button */}
+      <button
+        onClick={() => fileInputRef.current?.click()}
+        disabled={isUploading}
+        className="w-full flex items-center justify-center gap-2 px-3 py-2 rounded-lg
+                   border border-dashed border-dark-600 text-dark-400 text-sm
+                   hover:border-primary-400/50 hover:text-primary-400 hover:bg-primary-400/5
+                   disabled:opacity-50 transition-colors"
+      >
+        {isUploading ? (
+          <Loader2 className="w-4 h-4 animate-spin" />
+        ) : (
+          <Upload className="w-4 h-4" />
+        )}
+        {isUploading ? 'Uploading...' : 'Add context files'}
+      </button>
+      
+      <input
+        ref={fileInputRef}
+        type="file"
+        multiple
+        accept={VALID_EXTENSIONS.join(',')}
+        onChange={handleFileSelect}
+        className="hidden"
+      />
+    </div>
+  );
+};
+
+export default StudioContextFiles;

atomizer-dashboard/frontend/src/components/studio/StudioDropZone.tsx

@@ -0,0 +1,242 @@
+/**
+ * StudioDropZone - Smart file drop zone for Studio
+ * 
+ * Handles both model files (.sim, .prt, .fem) and context files (.pdf, .md, .txt)
+ */
+
+import React, { useState, useCallback, useRef } from 'react';
+import { Upload, X, Loader2, AlertCircle, CheckCircle, File } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface StudioDropZoneProps {
+  draftId: string;
+  type: 'model' | 'context';
+  files: string[];
+  onUploadComplete: () => void;
+}
+
+interface FileStatus {
+  file: File;
+  status: 'pending' | 'uploading' | 'success' | 'error';
+  message?: string;
+}
+
+const MODEL_EXTENSIONS = ['.prt', '.sim', '.fem', '.afem'];
+const CONTEXT_EXTENSIONS = ['.md', '.txt', '.pdf', '.json', '.csv', '.docx'];
+
+export const StudioDropZone: React.FC<StudioDropZoneProps> = ({
+  draftId,
+  type,
+  files,
+  onUploadComplete,
+}) => {
+  const [isDragging, setIsDragging] = useState(false);
+  const [pendingFiles, setPendingFiles] = useState<FileStatus[]>([]);
+  const [isUploading, setIsUploading] = useState(false);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+  
+  const validExtensions = type === 'model' ? MODEL_EXTENSIONS : CONTEXT_EXTENSIONS;
+  
+  const validateFile = (file: File): { valid: boolean; reason?: string } => {
+    const ext = '.' + file.name.split('.').pop()?.toLowerCase();
+    if (!validExtensions.includes(ext)) {
+      return { valid: false, reason: `Invalid type: ${ext}` };
+    }
+    if (file.size > 500 * 1024 * 1024) {
+      return { valid: false, reason: 'File too large (max 500MB)' };
+    }
+    return { valid: true };
+  };
+  
+  const handleDragEnter = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(true);
+  }, []);
+  
+  const handleDragLeave = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(false);
+  }, []);
+  
+  const handleDragOver = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+  }, []);
+  
+  const addFiles = useCallback((newFiles: File[]) => {
+    const validFiles: FileStatus[] = [];
+    
+    for (const file of newFiles) {
+      if (pendingFiles.some(f => f.file.name === file.name)) {
+        continue;
+      }
+      
+      const validation = validateFile(file);
+      validFiles.push({
+        file,
+        status: validation.valid ? 'pending' : 'error',
+        message: validation.reason,
+      });
+    }
+    
+    setPendingFiles(prev => [...prev, ...validFiles]);
+  }, [pendingFiles, validExtensions]);
+  
+  const handleDrop = useCallback((e: React.DragEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsDragging(false);
+    addFiles(Array.from(e.dataTransfer.files));
+  }, [addFiles]);
+  
+  const handleFileSelect = useCallback((e: React.ChangeEvent<HTMLInputElement>) => {
+    addFiles(Array.from(e.target.files || []));
+    e.target.value = '';
+  }, [addFiles]);
+  
+  const removeFile = (index: number) => {
+    setPendingFiles(prev => prev.filter((_, i) => i !== index));
+  };
+  
+  const uploadFiles = async () => {
+    const toUpload = pendingFiles.filter(f => f.status === 'pending');
+    if (toUpload.length === 0) return;
+    
+    setIsUploading(true);
+    
+    try {
+      const uploadFn = type === 'model' 
+        ? intakeApi.uploadFiles 
+        : intakeApi.uploadContextFiles;
+      
+      const response = await uploadFn(draftId, toUpload.map(f => f.file));
+      
+      const results = new Map(
+        response.uploaded_files.map(f => [f.name, f.status === 'uploaded'])
+      );
+      
+      setPendingFiles(prev => prev.map(f => {
+        if (f.status !== 'pending') return f;
+        const success = results.get(f.file.name);
+        return {
+          ...f,
+          status: success ? 'success' : 'error',
+          message: success ? undefined : 'Upload failed',
+        };
+      }));
+      
+      setTimeout(() => {
+        setPendingFiles(prev => prev.filter(f => f.status !== 'success'));
+        onUploadComplete();
+      }, 1000);
+      
+    } catch (err) {
+      setPendingFiles(prev => prev.map(f => 
+        f.status === 'pending' 
+          ? { ...f, status: 'error', message: 'Upload failed' }
+          : f
+      ));
+    } finally {
+      setIsUploading(false);
+    }
+  };
+  
+  // Auto-upload when files are added
+  React.useEffect(() => {
+    const pending = pendingFiles.filter(f => f.status === 'pending');
+    if (pending.length > 0 && !isUploading) {
+      uploadFiles();
+    }
+  }, [pendingFiles, isUploading]);
+  
+  return (
+    <div className="space-y-2">
+      {/* Drop Zone */}
+      <div
+        onDragEnter={handleDragEnter}
+        onDragLeave={handleDragLeave}
+        onDragOver={handleDragOver}
+        onDrop={handleDrop}
+        onClick={() => fileInputRef.current?.click()}
+        className={`
+          relative border-2 border-dashed rounded-lg p-4 cursor-pointer
+          transition-all duration-200 text-center
+          ${isDragging 
+            ? 'border-primary-400 bg-primary-400/5' 
+            : 'border-dark-600 hover:border-primary-400/50 hover:bg-white/5'
+          }
+        `}
+      >
+        <div className={`w-8 h-8 rounded-full flex items-center justify-center mx-auto mb-2
+                        ${isDragging ? 'bg-primary-400/20 text-primary-400' : 'bg-dark-700 text-dark-400'}`}>
+          <Upload className="w-4 h-4" />
+        </div>
+        <p className="text-sm text-dark-300">
+          {isDragging ? 'Drop files here' : 'Drop or click to add'}
+        </p>
+        <p className="text-xs text-dark-500 mt-1">
+          {validExtensions.join(', ')}
+        </p>
+      </div>
+      
+      {/* Existing Files */}
+      {files.length > 0 && (
+        <div className="space-y-1">
+          {files.map((name, i) => (
+            <div
+              key={i}
+              className="flex items-center gap-2 px-2 py-1.5 rounded bg-dark-700/50 text-sm"
+            >
+              <File className="w-3.5 h-3.5 text-dark-400" />
+              <span className="text-dark-200 truncate flex-1">{name}</span>
+              <CheckCircle className="w-3.5 h-3.5 text-green-400" />
+            </div>
+          ))}
+        </div>
+      )}
+      
+      {/* Pending Files */}
+      {pendingFiles.length > 0 && (
+        <div className="space-y-1">
+          {pendingFiles.map((f, i) => (
+            <div
+              key={i}
+              className={`flex items-center gap-2 px-2 py-1.5 rounded text-sm
+                         ${f.status === 'error' ? 'bg-red-500/10' : 
+                           f.status === 'success' ? 'bg-green-500/10' : 'bg-dark-700'}`}
+            >
+              {f.status === 'pending' && <Loader2 className="w-3.5 h-3.5 text-primary-400 animate-spin" />}
+              {f.status === 'uploading' && <Loader2 className="w-3.5 h-3.5 text-primary-400 animate-spin" />}
+              {f.status === 'success' && <CheckCircle className="w-3.5 h-3.5 text-green-400" />}
+              {f.status === 'error' && <AlertCircle className="w-3.5 h-3.5 text-red-400" />}
+              <span className={`truncate flex-1 ${f.status === 'error' ? 'text-red-400' : 'text-dark-200'}`}>
+                {f.file.name}
+              </span>
+              {f.message && (
+                <span className="text-xs text-red-400">({f.message})</span>
+              )}
+              {f.status === 'pending' && (
+                <button onClick={(e) => { e.stopPropagation(); removeFile(i); }} className="p-0.5 hover:bg-white/10 rounded">
+                  <X className="w-3 h-3 text-dark-400" />
+                </button>
+              )}
+            </div>
+          ))}
+        </div>
+      )}
+      
+      <input
+        ref={fileInputRef}
+        type="file"
+        multiple
+        accept={validExtensions.join(',')}
+        onChange={handleFileSelect}
+        className="hidden"
+      />
+    </div>
+  );
+};
+
+export default StudioDropZone;

atomizer-dashboard/frontend/src/components/studio/StudioParameterList.tsx

@@ -0,0 +1,172 @@
+/**
+ * StudioParameterList - Display and add discovered parameters as design variables
+ */
+
+import React, { useState, useEffect } from 'react';
+import { Plus, Check, SlidersHorizontal, Loader2 } from 'lucide-react';
+import { intakeApi } from '../../api/intake';
+
+interface Expression {
+  name: string;
+  value: number | null;
+  units: string | null;
+  is_candidate: boolean;
+  confidence: number;
+}
+
+interface StudioParameterListProps {
+  draftId: string;
+  onParameterAdded: () => void;
+}
+
+export const StudioParameterList: React.FC<StudioParameterListProps> = ({
+  draftId,
+  onParameterAdded,
+}) => {
+  const [expressions, setExpressions] = useState<Expression[]>([]);
+  const [addedParams, setAddedParams] = useState<Set<string>>(new Set());
+  const [adding, setAdding] = useState<string | null>(null);
+  const [loading, setLoading] = useState(true);
+  
+  // Load expressions from spec introspection
+  useEffect(() => {
+    loadExpressions();
+  }, [draftId]);
+  
+  const loadExpressions = async () => {
+    setLoading(true);
+    try {
+      const data = await intakeApi.getStudioDraft(draftId);
+      const introspection = (data.spec as any)?.model?.introspection;
+      
+      if (introspection?.expressions) {
+        setExpressions(introspection.expressions);
+        
+        // Check which are already added as DVs
+        const existingDVs = new Set<string>(
+          ((data.spec as any)?.design_variables || []).map((dv: any) => dv.expression_name as string)
+        );
+        setAddedParams(existingDVs);
+      }
+    } catch (err) {
+      console.error('Failed to load expressions:', err);
+    } finally {
+      setLoading(false);
+    }
+  };
+  
+  const addAsDesignVariable = async (expressionName: string) => {
+    setAdding(expressionName);
+    
+    try {
+      await intakeApi.createDesignVariables(draftId, [expressionName]);
+      setAddedParams(prev => new Set([...prev, expressionName]));
+      onParameterAdded();
+    } catch (err) {
+      console.error('Failed to add design variable:', err);
+    } finally {
+      setAdding(null);
+    }
+  };
+  
+  // Sort: candidates first, then by confidence
+  const sortedExpressions = [...expressions].sort((a, b) => {
+    if (a.is_candidate !== b.is_candidate) {
+      return b.is_candidate ? 1 : -1;
+    }
+    return (b.confidence || 0) - (a.confidence || 0);
+  });
+  
+  // Show only candidates by default, with option to show all
+  const [showAll, setShowAll] = useState(false);
+  const displayExpressions = showAll 
+    ? sortedExpressions 
+    : sortedExpressions.filter(e => e.is_candidate);
+  
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center py-4">
+        <Loader2 className="w-5 h-5 text-primary-400 animate-spin" />
+      </div>
+    );
+  }
+  
+  if (expressions.length === 0) {
+    return (
+      <p className="text-xs text-dark-500 italic py-2">
+        No expressions found. Try running introspection.
+      </p>
+    );
+  }
+  
+  const candidateCount = expressions.filter(e => e.is_candidate).length;
+  
+  return (
+    <div className="space-y-2">
+      {/* Header with toggle */}
+      <div className="flex items-center justify-between text-xs text-dark-400">
+        <span>{candidateCount} candidates</span>
+        <button
+          onClick={() => setShowAll(!showAll)}
+          className="hover:text-primary-400 transition-colors"
+        >
+          {showAll ? 'Show candidates only' : `Show all (${expressions.length})`}
+        </button>
+      </div>
+      
+      {/* Parameter List */}
+      <div className="space-y-1 max-h-48 overflow-y-auto">
+        {displayExpressions.map((expr) => {
+          const isAdded = addedParams.has(expr.name);
+          const isAdding = adding === expr.name;
+          
+          return (
+            <div
+              key={expr.name}
+              className={`flex items-center gap-2 px-2 py-1.5 rounded text-sm
+                         ${isAdded ? 'bg-green-500/10' : 'bg-dark-700/50 hover:bg-dark-700'}
+                         transition-colors`}
+            >
+              <SlidersHorizontal className="w-3.5 h-3.5 text-dark-400 flex-shrink-0" />
+              <div className="flex-1 min-w-0">
+                <span className={`block truncate ${isAdded ? 'text-green-400' : 'text-dark-200'}`}>
+                  {expr.name}
+                </span>
+                {expr.value !== null && (
+                  <span className="text-xs text-dark-500">
+                    = {expr.value}{expr.units ? ` ${expr.units}` : ''}
+                  </span>
+                )}
+              </div>
+              
+              {isAdded ? (
+                <Check className="w-4 h-4 text-green-400 flex-shrink-0" />
+              ) : (
+                <button
+                  onClick={() => addAsDesignVariable(expr.name)}
+                  disabled={isAdding}
+                  className="p-1 hover:bg-primary-400/20 rounded text-primary-400 transition-colors disabled:opacity-50"
+                  title="Add as design variable"
+                >
+                  {isAdding ? (
+                    <Loader2 className="w-3.5 h-3.5 animate-spin" />
+                  ) : (
+                    <Plus className="w-3.5 h-3.5" />
+                  )}
+                </button>
+              )}
+            </div>
+          );
+        })}
+      </div>
+      
+      {displayExpressions.length === 0 && (
+        <p className="text-xs text-dark-500 italic py-2">
+          No candidate parameters found. Click "Show all" to see all expressions.
+        </p>
+      )}
+    </div>
+  );
+};
+
+export default StudioParameterList;

atomizer-dashboard/frontend/src/components/studio/index.ts

@@ -0,0 +1,11 @@
+/**
+ * Studio Components Index
+ * 
+ * Export all Studio-related components.
+ */
+
+export { StudioDropZone } from './StudioDropZone';
+export { StudioParameterList } from './StudioParameterList';
+export { StudioContextFiles } from './StudioContextFiles';
+export { StudioChat } from './StudioChat';
+export { StudioBuildDialog } from './StudioBuildDialog';

atomizer-dashboard/frontend/src/hooks/useOptimizationStream.ts

@@ -0,0 +1,335 @@
+/**
+ * useOptimizationStream - Enhanced WebSocket hook for real-time optimization updates
+ * 
+ * This hook provides:
+ * - Real-time trial updates (no polling needed)
+ * - Best trial tracking
+ * - Progress tracking
+ * - Error detection and reporting
+ * - Integration with panel store for error display
+ * - Automatic reconnection
+ * 
+ * Usage:
+ * ```tsx
+ * const { 
+ *   isConnected, 
+ *   progress, 
+ *   bestTrial, 
+ *   recentTrials,
+ *   status 
+ * } = useOptimizationStream(studyId);
+ * ```
+ */
+
+import { useState, useEffect, useCallback, useRef } from 'react';
+import useWebSocket, { ReadyState } from 'react-use-websocket';
+import { usePanelStore } from './usePanelStore';
+
+// ============================================================================
+// Types
+// ============================================================================
+
+export interface TrialData {
+  trial_number: number;
+  trial_num: number;
+  objective: number | null;
+  values: number[];
+  params: Record<string, number>;
+  user_attrs: Record<string, unknown>;
+  source: 'FEA' | 'NN' | string;
+  start_time: string;
+  end_time: string;
+  study_name: string;
+  constraint_satisfied: boolean;
+}
+
+export interface ProgressData {
+  current: number;
+  total: number;
+  percentage: number;
+  fea_count: number;
+  nn_count: number;
+  timestamp: string;
+}
+
+export interface BestTrialData {
+  trial_number: number;
+  value: number;
+  params: Record<string, number>;
+  improvement: number;
+}
+
+export interface ParetoData {
+  pareto_front: Array<{
+    trial_number: number;
+    values: number[];
+    params: Record<string, number>;
+    constraint_satisfied: boolean;
+    source: string;
+  }>;
+  count: number;
+}
+
+export type OptimizationStatus = 'disconnected' | 'connecting' | 'connected' | 'running' | 'paused' | 'completed' | 'failed';
+
+export interface OptimizationStreamState {
+  isConnected: boolean;
+  status: OptimizationStatus;
+  progress: ProgressData | null;
+  bestTrial: BestTrialData | null;
+  recentTrials: TrialData[];
+  paretoFront: ParetoData | null;
+  lastUpdate: number | null;
+  error: string | null;
+}
+
+// ============================================================================
+// Hook
+// ============================================================================
+
+interface UseOptimizationStreamOptions {
+  /** Maximum number of recent trials to keep */
+  maxRecentTrials?: number;
+  /** Callback when a new trial completes */
+  onTrialComplete?: (trial: TrialData) => void;
+  /** Callback when a new best is found */
+  onNewBest?: (best: BestTrialData) => void;
+  /** Callback on progress update */
+  onProgress?: (progress: ProgressData) => void;
+  /** Whether to auto-report errors to the error panel */
+  autoReportErrors?: boolean;
+}
+
+export function useOptimizationStream(
+  studyId: string | null | undefined,
+  options: UseOptimizationStreamOptions = {}
+) {
+  const {
+    maxRecentTrials = 20,
+    onTrialComplete,
+    onNewBest,
+    onProgress,
+    autoReportErrors = true,
+  } = options;
+
+  // Panel store for error reporting
+  const { addError } = usePanelStore();
+
+  // State
+  const [state, setState] = useState<OptimizationStreamState>({
+    isConnected: false,
+    status: 'disconnected',
+    progress: null,
+    bestTrial: null,
+    recentTrials: [],
+    paretoFront: null,
+    lastUpdate: null,
+    error: null,
+  });
+
+  // Track last error timestamp to avoid duplicates
+  const lastErrorTime = useRef<number>(0);
+
+  // Build WebSocket URL
+  const socketUrl = studyId
+    ? `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${
+        import.meta.env.DEV ? 'localhost:8001' : window.location.host
+      }/api/ws/optimization/${encodeURIComponent(studyId)}`
+    : null;
+
+  // WebSocket connection
+  const { sendMessage, lastMessage, readyState } = useWebSocket(socketUrl, {
+    shouldReconnect: () => true,
+    reconnectAttempts: 10,
+    reconnectInterval: 3000,
+    onOpen: () => {
+      console.log('[OptStream] Connected to optimization stream');
+      setState(prev => ({ ...prev, isConnected: true, status: 'connected', error: null }));
+    },
+    onClose: () => {
+      console.log('[OptStream] Disconnected from optimization stream');
+      setState(prev => ({ ...prev, isConnected: false, status: 'disconnected' }));
+    },
+    onError: (event) => {
+      console.error('[OptStream] WebSocket error:', event);
+      setState(prev => ({ ...prev, error: 'WebSocket connection error' }));
+    },
+  });
+
+  // Update connection status
+  useEffect(() => {
+    const statusMap: Record<ReadyState, OptimizationStatus> = {
+      [ReadyState.CONNECTING]: 'connecting',
+      [ReadyState.OPEN]: 'connected',
+      [ReadyState.CLOSING]: 'disconnected',
+      [ReadyState.CLOSED]: 'disconnected',
+      [ReadyState.UNINSTANTIATED]: 'disconnected',
+    };
+    
+    setState(prev => ({
+      ...prev,
+      isConnected: readyState === ReadyState.OPEN,
+      status: prev.status === 'running' || prev.status === 'completed' || prev.status === 'failed'
+        ? prev.status
+        : statusMap[readyState] || 'disconnected',
+    }));
+  }, [readyState]);
+
+  // Process incoming messages
+  useEffect(() => {
+    if (!lastMessage?.data) return;
+
+    try {
+      const message = JSON.parse(lastMessage.data);
+      const { type, data } = message;
+
+      switch (type) {
+        case 'connected':
+          console.log('[OptStream] Connection confirmed:', data.message);
+          break;
+
+        case 'trial_completed':
+          handleTrialComplete(data as TrialData);
+          break;
+
+        case 'new_best':
+          handleNewBest(data as BestTrialData);
+          break;
+
+        case 'progress':
+          handleProgress(data as ProgressData);
+          break;
+
+        case 'pareto_update':
+          handleParetoUpdate(data as ParetoData);
+          break;
+
+        case 'heartbeat':
+        case 'pong':
+          // Keep-alive messages
+          break;
+
+        case 'error':
+          handleError(data);
+          break;
+
+        default:
+          console.log('[OptStream] Unknown message type:', type, data);
+      }
+    } catch (e) {
+      console.error('[OptStream] Failed to parse message:', e);
+    }
+  }, [lastMessage]);
+
+  // Handler functions
+  const handleTrialComplete = useCallback((trial: TrialData) => {
+    setState(prev => {
+      const newTrials = [trial, ...prev.recentTrials].slice(0, maxRecentTrials);
+      return {
+        ...prev,
+        recentTrials: newTrials,
+        lastUpdate: Date.now(),
+        status: 'running',
+      };
+    });
+    
+    onTrialComplete?.(trial);
+  }, [maxRecentTrials, onTrialComplete]);
+
+  const handleNewBest = useCallback((best: BestTrialData) => {
+    setState(prev => ({
+      ...prev,
+      bestTrial: best,
+      lastUpdate: Date.now(),
+    }));
+    
+    onNewBest?.(best);
+  }, [onNewBest]);
+
+  const handleProgress = useCallback((progress: ProgressData) => {
+    setState(prev => {
+      // Determine status based on progress
+      let status: OptimizationStatus = prev.status;
+      if (progress.current > 0 && progress.current < progress.total) {
+        status = 'running';
+      } else if (progress.current >= progress.total) {
+        status = 'completed';
+      }
+      
+      return {
+        ...prev,
+        progress,
+        status,
+        lastUpdate: Date.now(),
+      };
+    });
+    
+    onProgress?.(progress);
+  }, [onProgress]);
+
+  const handleParetoUpdate = useCallback((pareto: ParetoData) => {
+    setState(prev => ({
+      ...prev,
+      paretoFront: pareto,
+      lastUpdate: Date.now(),
+    }));
+  }, []);
+
+  const handleError = useCallback((errorData: { message: string; details?: string; trial?: number }) => {
+    const now = Date.now();
+    
+    // Avoid duplicate errors within 5 seconds
+    if (now - lastErrorTime.current < 5000) return;
+    lastErrorTime.current = now;
+
+    setState(prev => ({
+      ...prev,
+      error: errorData.message,
+      status: 'failed',
+    }));
+
+    if (autoReportErrors) {
+      addError({
+        type: 'system_error',
+        message: errorData.message,
+        details: errorData.details,
+        trial: errorData.trial,
+        recoverable: true,
+        suggestions: ['Check the optimization logs', 'Try restarting the optimization'],
+        timestamp: now,
+      });
+    }
+  }, [autoReportErrors, addError]);
+
+  // Send ping to keep connection alive
+  useEffect(() => {
+    if (readyState !== ReadyState.OPEN) return;
+
+    const interval = setInterval(() => {
+      sendMessage(JSON.stringify({ type: 'ping' }));
+    }, 25000); // Ping every 25 seconds
+
+    return () => clearInterval(interval);
+  }, [readyState, sendMessage]);
+
+  // Reset state when study changes
+  useEffect(() => {
+    setState({
+      isConnected: false,
+      status: 'disconnected',
+      progress: null,
+      bestTrial: null,
+      recentTrials: [],
+      paretoFront: null,
+      lastUpdate: null,
+      error: null,
+    });
+  }, [studyId]);
+
+  return {
+    ...state,
+    sendPing: () => sendMessage(JSON.stringify({ type: 'ping' })),
+  };
+}
+
+export default useOptimizationStream;

atomizer-dashboard/frontend/src/hooks/usePanelStore.ts

@@ -0,0 +1,375 @@
+/**
+ * usePanelStore - Centralized state management for canvas panels
+ * 
+ * This store manages the visibility and state of all panels in the canvas view.
+ * Panels persist their state even when the user clicks elsewhere on the canvas.
+ * 
+ * Panel Types:
+ * - introspection: Model introspection results (floating, draggable)
+ * - validation: Spec validation errors/warnings (floating)
+ * - results: Trial results details (floating)
+ * - error: Error display with recovery options (floating)
+ */
+
+import { create } from 'zustand';
+import { persist } from 'zustand/middleware';
+
+// ============================================================================
+// Types
+// ============================================================================
+
+export interface IntrospectionData {
+  filePath: string;
+  studyId?: string;
+  selectedFile?: string;
+  result?: Record<string, unknown>;
+  isLoading?: boolean;
+  error?: string | null;
+}
+
+export interface ValidationError {
+  code: string;
+  severity: 'error' | 'warning';
+  path: string;
+  message: string;
+  suggestion?: string;
+  nodeId?: string;
+}
+
+export interface ValidationData {
+  valid: boolean;
+  errors: ValidationError[];
+  warnings: ValidationError[];
+  checkedAt: number;
+}
+
+export interface OptimizationError {
+  type: 'nx_crash' | 'solver_fail' | 'extractor_error' | 'config_error' | 'system_error' | 'unknown';
+  trial?: number;
+  message: string;
+  details?: string;
+  recoverable: boolean;
+  suggestions: string[];
+  timestamp: number;
+}
+
+export interface TrialResultData {
+  trialNumber: number;
+  params: Record<string, number>;
+  objectives: Record<string, number>;
+  constraints?: Record<string, { value: number; feasible: boolean }>;
+  isFeasible: boolean;
+  isBest: boolean;
+  timestamp: number;
+}
+
+export interface PanelPosition {
+  x: number;
+  y: number;
+}
+
+export interface PanelState {
+  open: boolean;
+  position?: PanelPosition;
+  minimized?: boolean;
+}
+
+export interface IntrospectionPanelState extends PanelState {
+  data?: IntrospectionData;
+}
+
+export interface ValidationPanelState extends PanelState {
+  data?: ValidationData;
+}
+
+export interface ErrorPanelState extends PanelState {
+  errors: OptimizationError[];
+}
+
+export interface ResultsPanelState extends PanelState {
+  data?: TrialResultData;
+}
+
+// ============================================================================
+// Store Interface
+// ============================================================================
+
+interface PanelStore {
+  // Panel states
+  introspection: IntrospectionPanelState;
+  validation: ValidationPanelState;
+  error: ErrorPanelState;
+  results: ResultsPanelState;
+  
+  // Generic panel actions
+  openPanel: (panel: 'introspection' | 'validation' | 'error' | 'results') => void;
+  closePanel: (panel: 'introspection' | 'validation' | 'error' | 'results') => void;
+  togglePanel: (panel: 'introspection' | 'validation' | 'error' | 'results') => void;
+  minimizePanel: (panel: 'introspection' | 'validation' | 'error' | 'results') => void;
+  setPanelPosition: (panel: 'introspection' | 'validation' | 'error' | 'results', position: PanelPosition) => void;
+  
+  // Introspection-specific actions
+  setIntrospectionData: (data: IntrospectionData) => void;
+  updateIntrospectionResult: (result: Record<string, unknown>) => void;
+  setIntrospectionLoading: (loading: boolean) => void;
+  setIntrospectionError: (error: string | null) => void;
+  setIntrospectionFile: (fileName: string) => void;
+  
+  // Validation-specific actions
+  setValidationData: (data: ValidationData) => void;
+  clearValidation: () => void;
+  
+  // Error-specific actions
+  addError: (error: OptimizationError) => void;
+  clearErrors: () => void;
+  dismissError: (timestamp: number) => void;
+  
+  // Results-specific actions
+  setTrialResult: (data: TrialResultData) => void;
+  clearTrialResult: () => void;
+  
+  // Utility
+  closeAllPanels: () => void;
+  hasOpenPanels: () => boolean;
+}
+
+// ============================================================================
+// Default States
+// ============================================================================
+
+const defaultIntrospection: IntrospectionPanelState = {
+  open: false,
+  position: { x: 100, y: 100 },
+  minimized: false,
+  data: undefined,
+};
+
+const defaultValidation: ValidationPanelState = {
+  open: false,
+  position: { x: 150, y: 150 },
+  minimized: false,
+  data: undefined,
+};
+
+const defaultError: ErrorPanelState = {
+  open: false,
+  position: { x: 200, y: 100 },
+  minimized: false,
+  errors: [],
+};
+
+const defaultResults: ResultsPanelState = {
+  open: false,
+  position: { x: 250, y: 150 },
+  minimized: false,
+  data: undefined,
+};
+
+// ============================================================================
+// Store Implementation
+// ============================================================================
+
+export const usePanelStore = create<PanelStore>()(
+  persist(
+    (set, get) => ({
+      // Initial states
+      introspection: defaultIntrospection,
+      validation: defaultValidation,
+      error: defaultError,
+      results: defaultResults,
+      
+      // Generic panel actions
+      openPanel: (panel) => set((state) => ({
+        [panel]: { ...state[panel], open: true, minimized: false }
+      })),
+      
+      closePanel: (panel) => set((state) => ({
+        [panel]: { ...state[panel], open: false }
+      })),
+      
+      togglePanel: (panel) => set((state) => ({
+        [panel]: { ...state[panel], open: !state[panel].open, minimized: false }
+      })),
+      
+      minimizePanel: (panel) => set((state) => ({
+        [panel]: { ...state[panel], minimized: !state[panel].minimized }
+      })),
+      
+      setPanelPosition: (panel, position) => set((state) => ({
+        [panel]: { ...state[panel], position }
+      })),
+      
+      // Introspection actions
+      setIntrospectionData: (data) => set((state) => ({
+        introspection: { 
+          ...state.introspection, 
+          open: true, 
+          data 
+        }
+      })),
+      
+      updateIntrospectionResult: (result) => set((state) => ({
+        introspection: {
+          ...state.introspection,
+          data: state.introspection.data 
+            ? { ...state.introspection.data, result, isLoading: false, error: null }
+            : undefined
+        }
+      })),
+      
+      setIntrospectionLoading: (loading) => set((state) => ({
+        introspection: {
+          ...state.introspection,
+          data: state.introspection.data
+            ? { ...state.introspection.data, isLoading: loading }
+            : undefined
+        }
+      })),
+      
+      setIntrospectionError: (error) => set((state) => ({
+        introspection: {
+          ...state.introspection,
+          data: state.introspection.data
+            ? { ...state.introspection.data, error, isLoading: false }
+            : undefined
+        }
+      })),
+      
+      setIntrospectionFile: (fileName) => set((state) => ({
+        introspection: {
+          ...state.introspection,
+          data: state.introspection.data
+            ? { ...state.introspection.data, selectedFile: fileName }
+            : undefined
+        }
+      })),
+      
+      // Validation actions
+      setValidationData: (data) => set((state) => ({
+        validation: { 
+          ...state.validation, 
+          open: true, 
+          data 
+        }
+      })),
+      
+      clearValidation: () => set((state) => ({
+        validation: { 
+          ...state.validation, 
+          data: undefined 
+        }
+      })),
+      
+      // Error actions
+      addError: (error) => set((state) => ({
+        error: {
+          ...state.error,
+          open: true,
+          errors: [...state.error.errors, error]
+        }
+      })),
+      
+      clearErrors: () => set((state) => ({
+        error: {
+          ...state.error,
+          errors: [],
+          open: false
+        }
+      })),
+      
+      dismissError: (timestamp) => set((state) => {
+        const newErrors = state.error.errors.filter(e => e.timestamp !== timestamp);
+        return {
+          error: {
+            ...state.error,
+            errors: newErrors,
+            open: newErrors.length > 0
+          }
+        };
+      }),
+      
+      // Results actions
+      setTrialResult: (data) => set((state) => ({
+        results: {
+          ...state.results,
+          open: true,
+          data
+        }
+      })),
+      
+      clearTrialResult: () => set((state) => ({
+        results: {
+          ...state.results,
+          data: undefined,
+          open: false
+        }
+      })),
+      
+      // Utility
+      closeAllPanels: () => set({
+        introspection: { ...get().introspection, open: false },
+        validation: { ...get().validation, open: false },
+        error: { ...get().error, open: false },
+        results: { ...get().results, open: false },
+      }),
+      
+      hasOpenPanels: () => {
+        const state = get();
+        return state.introspection.open || 
+               state.validation.open || 
+               state.error.open || 
+               state.results.open;
+      },
+    }),
+    {
+      name: 'atomizer-panel-store',
+      // Only persist certain fields (not loading states or errors)
+      partialize: (state) => ({
+        introspection: {
+          position: state.introspection.position,
+          // Don't persist open state - start fresh each session
+        },
+        validation: {
+          position: state.validation.position,
+        },
+        error: {
+          position: state.error.position,
+        },
+        results: {
+          position: state.results.position,
+        },
+      }),
+    }
+  )
+);
+
+// ============================================================================
+// Selector Hooks (for convenience)
+// ============================================================================
+
+export const useIntrospectionPanel = () => usePanelStore((state) => state.introspection);
+export const useValidationPanel = () => usePanelStore((state) => state.validation);
+export const useErrorPanel = () => usePanelStore((state) => state.error);
+export const useResultsPanel = () => usePanelStore((state) => state.results);
+
+// Actions
+export const usePanelActions = () => usePanelStore((state) => ({
+  openPanel: state.openPanel,
+  closePanel: state.closePanel,
+  togglePanel: state.togglePanel,
+  minimizePanel: state.minimizePanel,
+  setPanelPosition: state.setPanelPosition,
+  setIntrospectionData: state.setIntrospectionData,
+  updateIntrospectionResult: state.updateIntrospectionResult,
+  setIntrospectionLoading: state.setIntrospectionLoading,
+  setIntrospectionError: state.setIntrospectionError,
+  setIntrospectionFile: state.setIntrospectionFile,
+  setValidationData: state.setValidationData,
+  clearValidation: state.clearValidation,
+  addError: state.addError,
+  clearErrors: state.clearErrors,
+  dismissError: state.dismissError,
+  setTrialResult: state.setTrialResult,
+  clearTrialResult: state.clearTrialResult,
+  closeAllPanels: state.closeAllPanels,
+}));

atomizer-dashboard/frontend/src/hooks/useResizablePanel.ts

@@ -0,0 +1,156 @@
+/**
+ * useResizablePanel - Hook for creating resizable panels with persistence
+ * 
+ * Features:
+ * - Drag to resize
+ * - Min/max constraints
+ * - localStorage persistence
+ * - Double-click to reset to default
+ */
+
+import { useState, useCallback, useEffect, useRef } from 'react';
+
+export interface ResizablePanelConfig {
+  /** Unique key for localStorage persistence */
+  storageKey: string;
+  /** Default width in pixels */
+  defaultWidth: number;
+  /** Minimum width in pixels */
+  minWidth: number;
+  /** Maximum width in pixels */
+  maxWidth: number;
+  /** Side of the panel ('left' or 'right') - affects resize direction */
+  side: 'left' | 'right';
+}
+
+export interface ResizablePanelState {
+  /** Current width in pixels */
+  width: number;
+  /** Whether user is currently dragging */
+  isDragging: boolean;
+  /** Start drag handler - attach to resize handle mousedown */
+  startDrag: (e: React.MouseEvent) => void;
+  /** Reset to default width */
+  resetWidth: () => void;
+  /** Set width programmatically */
+  setWidth: (width: number) => void;
+}
+
+const STORAGE_PREFIX = 'atomizer-panel-';
+
+function getStoredWidth(key: string, defaultWidth: number): number {
+  if (typeof window === 'undefined') return defaultWidth;
+  
+  try {
+    const stored = localStorage.getItem(STORAGE_PREFIX + key);
+    if (stored) {
+      const parsed = parseInt(stored, 10);
+      if (!isNaN(parsed)) return parsed;
+    }
+  } catch {
+    // localStorage not available
+  }
+  return defaultWidth;
+}
+
+function storeWidth(key: string, width: number): void {
+  if (typeof window === 'undefined') return;
+  
+  try {
+    localStorage.setItem(STORAGE_PREFIX + key, String(width));
+  } catch {
+    // localStorage not available
+  }
+}
+
+export function useResizablePanel(config: ResizablePanelConfig): ResizablePanelState {
+  const { storageKey, defaultWidth, minWidth, maxWidth, side } = config;
+  
+  // Initialize from localStorage
+  const [width, setWidthState] = useState(() => {
+    const stored = getStoredWidth(storageKey, defaultWidth);
+    return Math.max(minWidth, Math.min(maxWidth, stored));
+  });
+  
+  const [isDragging, setIsDragging] = useState(false);
+  
+  // Track initial position for drag calculation
+  const dragStartRef = useRef<{ x: number; width: number } | null>(null);
+  
+  // Clamp width within bounds
+  const clampWidth = useCallback((w: number) => {
+    return Math.max(minWidth, Math.min(maxWidth, w));
+  }, [minWidth, maxWidth]);
+  
+  // Set width with clamping and persistence
+  const setWidth = useCallback((newWidth: number) => {
+    const clamped = clampWidth(newWidth);
+    setWidthState(clamped);
+    storeWidth(storageKey, clamped);
+  }, [clampWidth, storageKey]);
+  
+  // Reset to default
+  const resetWidth = useCallback(() => {
+    setWidth(defaultWidth);
+  }, [defaultWidth, setWidth]);
+  
+  // Start drag handler
+  const startDrag = useCallback((e: React.MouseEvent) => {
+    e.preventDefault();
+    setIsDragging(true);
+    dragStartRef.current = { x: e.clientX, width };
+  }, [width]);
+  
+  // Handle mouse move during drag
+  useEffect(() => {
+    if (!isDragging) return;
+    
+    const handleMouseMove = (e: MouseEvent) => {
+      if (!dragStartRef.current) return;
+      
+      const delta = e.clientX - dragStartRef.current.x;
+      
+      // For left panels, positive delta increases width
+      // For right panels, negative delta increases width
+      const newWidth = side === 'left'
+        ? dragStartRef.current.width + delta
+        : dragStartRef.current.width - delta;
+      
+      setWidthState(clampWidth(newWidth));
+    };
+    
+    const handleMouseUp = () => {
+      if (dragStartRef.current) {
+        // Persist the final width
+        storeWidth(storageKey, width);
+      }
+      setIsDragging(false);
+      dragStartRef.current = null;
+    };
+    
+    // Add listeners to document for smooth dragging
+    document.addEventListener('mousemove', handleMouseMove);
+    document.addEventListener('mouseup', handleMouseUp);
+    
+    // Change cursor globally during drag
+    document.body.style.cursor = 'col-resize';
+    document.body.style.userSelect = 'none';
+    
+    return () => {
+      document.removeEventListener('mousemove', handleMouseMove);
+      document.removeEventListener('mouseup', handleMouseUp);
+      document.body.style.cursor = '';
+      document.body.style.userSelect = '';
+    };
+  }, [isDragging, side, clampWidth, storageKey, width]);
+  
+  return {
+    width,
+    isDragging,
+    startDrag,
+    resetWidth,
+    setWidth,
+  };
+}
+
+export default useResizablePanel;

atomizer-dashboard/frontend/src/hooks/useSpecDraft.ts

@@ -0,0 +1,121 @@
+/**
+ * useSpecDraft (S2 Draft + Publish)
+ *
+ * Local autosave for AtomizerSpec so users don't lose work.
+ * "Publish" still uses useSpecStore.saveSpec() to write atomizer_spec.json.
+ *
+ * NOTE: This is a partial S2 implementation because the current store
+ * still patches the backend during edits. This draft layer still provides:
+ * - crash/refresh protection
+ * - explicit restore/discard prompt
+ */
+
+import { useCallback, useEffect, useMemo, useRef, useState } from 'react';
+import type { AtomizerSpec } from '../types/atomizer-spec';
+
+const draftKey = (studyId: string) => `atomizer:draft:${studyId}`;
+
+type DraftPayload = {
+  spec: AtomizerSpec;
+  baseHash: string | null;
+  updatedAt: number;
+};
+
+export function useSpecDraft(params: {
+  studyId: string | null | undefined;
+  spec: AtomizerSpec | null | undefined;
+  serverHash: string | null | undefined;
+  enabled?: boolean;
+}) {
+  const { studyId, spec, serverHash, enabled = true } = params;
+
+  const [hasDraft, setHasDraft] = useState(false);
+  const [draft, setDraft] = useState<DraftPayload | null>(null);
+
+  // Debounce writes
+  const writeTimer = useRef<number | null>(null);
+
+  const key = useMemo(() => (studyId ? draftKey(studyId) : null), [studyId]);
+
+  const loadDraft = useCallback(() => {
+    if (!enabled || !key) return null;
+    try {
+      const raw = localStorage.getItem(key);
+      if (!raw) return null;
+      const parsed = JSON.parse(raw) as DraftPayload;
+      if (!parsed?.spec) return null;
+      return parsed;
+    } catch {
+      return null;
+    }
+  }, [enabled, key]);
+
+  const discardDraft = useCallback(() => {
+    if (!enabled || !key) return;
+    localStorage.removeItem(key);
+    setHasDraft(false);
+    setDraft(null);
+  }, [enabled, key]);
+
+  const saveDraftNow = useCallback(
+    (payload: DraftPayload) => {
+      if (!enabled || !key) return;
+      try {
+        localStorage.setItem(key, JSON.stringify(payload));
+        setHasDraft(true);
+        setDraft(payload);
+      } catch {
+        // ignore storage failures
+      }
+    },
+    [enabled, key]
+  );
+
+  // Load draft on study change
+  useEffect(() => {
+    if (!enabled || !key) return;
+    const existing = loadDraft();
+    if (existing) {
+      setHasDraft(true);
+      setDraft(existing);
+    } else {
+      setHasDraft(false);
+      setDraft(null);
+    }
+  }, [enabled, key, loadDraft]);
+
+  // Autosave whenever spec changes
+  useEffect(() => {
+    if (!enabled || !key) return;
+    if (!studyId || !spec) return;
+
+    // Clear existing debounce
+    if (writeTimer.current) {
+      window.clearTimeout(writeTimer.current);
+      writeTimer.current = null;
+    }
+
+    writeTimer.current = window.setTimeout(() => {
+      saveDraftNow({ spec, baseHash: serverHash ?? null, updatedAt: Date.now() });
+    }, 750);
+
+    return () => {
+      if (writeTimer.current) {
+        window.clearTimeout(writeTimer.current);
+        writeTimer.current = null;
+      }
+    };
+  }, [enabled, key, studyId, spec, serverHash, saveDraftNow]);
+
+  return {
+    hasDraft,
+    draft,
+    discardDraft,
+    reloadDraft: () => {
+      const d = loadDraft();
+      setDraft(d);
+      setHasDraft(Boolean(d));
+      return d;
+    },
+  };
+}

atomizer-dashboard/frontend/src/hooks/useSpecStore.ts

@@ -63,6 +63,9 @@ interface SpecStoreActions {
   // WebSocket integration - set spec directly without API call
   setSpecFromWebSocket: (spec: AtomizerSpec, studyId?: string) => void;
 
+  // Local draft integration (S2) - set spec locally (no API call) and mark dirty
+  setSpecLocalDraft: (spec: AtomizerSpec, studyId?: string) => void;
+
   // Full spec operations
   saveSpec: (spec: AtomizerSpec) => Promise<void>;
   replaceSpec: (spec: AtomizerSpec) => Promise<void>;
@@ -402,6 +405,20 @@ export const useSpecStore = create<SpecStore>()(
         });
       },
 
+      // Set spec locally as a draft (no API call). This is used by DraftManager (S2).
+      // Marks the spec as dirty to indicate "not published".
+      setSpecLocalDraft: (spec: AtomizerSpec, studyId?: string) => {
+        const currentStudyId = studyId || get().studyId;
+        console.log('[useSpecStore] Setting spec from local draft:', spec.meta?.study_name);
+        set({
+          spec,
+          studyId: currentStudyId,
+          isLoading: false,
+          isDirty: true,
+          error: null,
+        });
+      },
+
       // =====================================================================
       // Full Spec Operations
       // =====================================================================

atomizer-dashboard/frontend/src/hooks/useSpecUndoRedo.ts

@@ -16,7 +16,7 @@
 
 import { useEffect, useRef } from 'react';
 import { useUndoRedo, UndoRedoResult } from './useUndoRedo';
-import { useSpecStore, useSpec, useSpecIsDirty } from './useSpecStore';
+import { useSpecStore, useSpec } from './useSpecStore';
 import { AtomizerSpec } from '../types/atomizer-spec';
 
 const STORAGE_KEY_PREFIX = 'atomizer-spec-history-';
@@ -28,7 +28,6 @@ export interface SpecUndoRedoResult extends UndoRedoResult<AtomizerSpec | null>
 
 export function useSpecUndoRedo(): SpecUndoRedoResult {
   const spec = useSpec();
-  const isDirty = useSpecIsDirty();
   const studyId = useSpecStore((state) => state.studyId);
   const lastSpecRef = useRef<AtomizerSpec | null>(null);
 
@@ -56,13 +55,21 @@ export function useSpecUndoRedo(): SpecUndoRedoResult {
     },
   });
 
-  // Record snapshot when spec changes (and is dirty)
+  // Record snapshot when spec changes
+  // Note: We removed the isDirty check because with auto-save, isDirty is always false
+  // after the API call completes. Instead, we compare the spec directly.
   useEffect(() => {
-    if (spec && isDirty && spec !== lastSpecRef.current) {
-      lastSpecRef.current = spec;
-      undoRedo.recordSnapshot();
+    if (spec && spec !== lastSpecRef.current) {
+      // Deep compare to avoid recording duplicate snapshots
+      const specStr = JSON.stringify(spec);
+      const lastStr = lastSpecRef.current ? JSON.stringify(lastSpecRef.current) : '';
+      
+      if (specStr !== lastStr) {
+        lastSpecRef.current = spec;
+        undoRedo.recordSnapshot();
+      }
     }
-  }, [spec, isDirty, undoRedo]);
+  }, [spec, undoRedo]);
 
   // Clear history when study changes
   useEffect(() => {

atomizer-dashboard/frontend/src/lib/canvas/schema.ts

@@ -16,6 +16,7 @@ export interface BaseNodeData {
   label: string;
   configured: boolean;
   errors?: string[];
+  resultValue?: number | string | null; // For Results Overlay
 }
 
 export interface ModelNodeData extends BaseNodeData {
@@ -24,9 +25,17 @@ export interface ModelNodeData extends BaseNodeData {
   fileType?: 'prt' | 'fem' | 'sim';
 }
 
+export type SolverEngine = 'nxnastran' | 'mscnastran' | 'python' | 'abaqus' | 'ansys';
+export type NastranSolutionType = 'SOL101' | 'SOL103' | 'SOL105' | 'SOL106' | 'SOL111' | 'SOL112' | 'SOL200';
+
 export interface SolverNodeData extends BaseNodeData {
   type: 'solver';
-  solverType?: 'SOL101' | 'SOL103' | 'SOL105' | 'SOL106' | 'SOL111' | 'SOL112';
+  /** Solver engine (nxnastran, mscnastran, python, etc.) */
+  engine?: SolverEngine;
+  /** Solution type for Nastran solvers */
+  solverType?: NastranSolutionType;
+  /** Python script path (for python engine) */
+  scriptPath?: string;
 }
 
 export interface DesignVarNodeData extends BaseNodeData {
@@ -98,6 +107,7 @@ export interface ObjectiveNodeData extends BaseNodeData {
   extractorRef?: string;  // Reference to extractor ID
   outputName?: string;    // Which output from the extractor
   penaltyWeight?: number; // For hard constraints (penalty method)
+  history?: number[];     // Recent values for sparkline visualization
 }
 
 export interface ConstraintNodeData extends BaseNodeData {
@@ -105,6 +115,7 @@ export interface ConstraintNodeData extends BaseNodeData {
   name?: string;
   operator?: '<' | '<=' | '>' | '>=' | '==';
   value?: number;
+  isFeasible?: boolean; // For Results Overlay
 }
 
 export interface AlgorithmNodeData extends BaseNodeData {

atomizer-dashboard/frontend/src/lib/spec.ts

@@ -0,0 +1,324 @@
+/**
+ * Spec ↔ Canvas converters
+ *
+ * AtomizerSpec v2.0 is the single source of truth.
+ * This module converts AtomizerSpec → ReactFlow nodes/edges for visualization.
+ *
+ * NOTE: Canvas edges are primarily for visual validation.
+ * The computation truth lives in objective.source / constraint.source.
+ */
+
+import type { Node, Edge } from 'reactflow';
+
+import type { AtomizerSpec, CanvasPosition, DesignVariable, Extractor, Objective, Constraint } from '../types/atomizer-spec';
+import type {
+  CanvasNodeData,
+  ModelNodeData,
+  SolverNodeData,
+  AlgorithmNodeData,
+  SurrogateNodeData,
+  DesignVarNodeData,
+  ExtractorNodeData,
+  ObjectiveNodeData,
+  ConstraintNodeData,
+} from './canvas/schema';
+
+// ---------------------------------------------------------------------------
+// Layout defaults (deterministic)
+// ---------------------------------------------------------------------------
+
+const DEFAULT_LAYOUT = {
+  startX: 80,
+  startY: 80,
+  colWidth: 260,
+  rowHeight: 110,
+  cols: {
+    designVar: 0,
+    model: 1,
+    solver: 2,
+    extractor: 3,
+    objective: 4,
+    constraint: 4,
+    algorithm: 5,
+    surrogate: 6,
+  } as const,
+};
+
+function toCanvasPosition(pos: CanvasPosition | undefined | null, fallback: CanvasPosition): CanvasPosition {
+  if (!pos) return fallback;
+  if (typeof pos.x !== 'number' || typeof pos.y !== 'number') return fallback;
+  return { x: pos.x, y: pos.y };
+}
+
+function makeFallbackPosition(col: number, row: number): CanvasPosition {
+  return {
+    x: DEFAULT_LAYOUT.startX + col * DEFAULT_LAYOUT.colWidth,
+    y: DEFAULT_LAYOUT.startY + row * DEFAULT_LAYOUT.rowHeight,
+  };
+}
+
+// ---------------------------------------------------------------------------
+// Synthetic nodes (always present)
+// ---------------------------------------------------------------------------
+
+export function isSyntheticNodeId(id: string): boolean {
+  return id === 'model' || id === 'solver' || id === 'algorithm' || id === 'surrogate';
+}
+
+function makeModelNode(spec: AtomizerSpec): Node<ModelNodeData> {
+  const pos = toCanvasPosition(
+    spec.model?.sim?.path ? (spec.model as any)?.canvas_position : undefined,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.model, 0)
+  );
+
+  return {
+    id: 'model',
+    type: 'model',
+    position: pos,
+    data: {
+      type: 'model',
+      label: spec.meta?.study_name || 'Model',
+      configured: Boolean(spec.model?.sim?.path),
+      filePath: spec.model?.sim?.path,
+      fileType: 'sim',
+    },
+  };
+}
+
+function makeSolverNode(spec: AtomizerSpec): Node<SolverNodeData> {
+  const sim = spec.model?.sim;
+  const pos = makeFallbackPosition(DEFAULT_LAYOUT.cols.solver, 0);
+
+  return {
+    id: 'solver',
+    type: 'solver',
+    position: pos,
+    data: {
+      type: 'solver',
+      label: sim?.engine ? `Solver (${sim.engine})` : 'Solver',
+      configured: Boolean(sim?.engine || sim?.solution_type),
+      engine: sim?.engine as any,
+      solverType: sim?.solution_type as any,
+      scriptPath: sim?.script_path,
+    },
+  };
+}
+
+function makeAlgorithmNode(spec: AtomizerSpec): Node<AlgorithmNodeData> {
+  const algo = spec.optimization?.algorithm;
+  const budget = spec.optimization?.budget;
+  const pos = toCanvasPosition(
+    spec.optimization?.canvas_position,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.algorithm, 0)
+  );
+
+  return {
+    id: 'algorithm',
+    type: 'algorithm',
+    position: pos,
+    data: {
+      type: 'algorithm',
+      label: algo?.type || 'Algorithm',
+      configured: Boolean(algo?.type),
+      method: (algo?.type as any) || 'TPE',
+      maxTrials: budget?.max_trials,
+      sigma0: (algo?.config as any)?.sigma0,
+      restartStrategy: (algo?.config as any)?.restart_strategy,
+    },
+  };
+}
+
+function makeSurrogateNode(spec: AtomizerSpec): Node<SurrogateNodeData> {
+  const surrogate = spec.optimization?.surrogate;
+  const pos = makeFallbackPosition(DEFAULT_LAYOUT.cols.surrogate, 0);
+
+  const enabled = Boolean(surrogate?.enabled);
+
+  return {
+    id: 'surrogate',
+    type: 'surrogate',
+    position: pos,
+    data: {
+      type: 'surrogate',
+      label: enabled ? 'Surrogate (enabled)' : 'Surrogate',
+      configured: true,
+      enabled,
+      modelType: (surrogate?.type as any) || 'MLP',
+      minTrials: surrogate?.config?.min_training_samples,
+    },
+  };
+}
+
+// ---------------------------------------------------------------------------
+// Array-backed nodes
+// ---------------------------------------------------------------------------
+
+function makeDesignVarNode(dv: DesignVariable, index: number): Node<DesignVarNodeData> {
+  const pos = toCanvasPosition(
+    dv.canvas_position,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.designVar, index)
+  );
+
+  return {
+    id: dv.id,
+    type: 'designVar',
+    position: pos,
+    data: {
+      type: 'designVar',
+      label: dv.name,
+      configured: Boolean(dv.expression_name),
+      expressionName: dv.expression_name,
+      minValue: dv.bounds?.min,
+      maxValue: dv.bounds?.max,
+      baseline: dv.baseline,
+      unit: dv.units,
+      enabled: dv.enabled,
+    },
+  };
+}
+
+function makeExtractorNode(ext: Extractor, index: number): Node<ExtractorNodeData> {
+  const pos = toCanvasPosition(
+    ext.canvas_position,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.extractor, index)
+  );
+
+  return {
+    id: ext.id,
+    type: 'extractor',
+    position: pos,
+    data: {
+      type: 'extractor',
+      label: ext.name,
+      configured: true,
+      extractorId: ext.id,
+      extractorName: ext.name,
+      extractorType: ext.type as any,
+      config: ext.config as any,
+      outputNames: (ext.outputs || []).map((o) => o.name),
+      // Convenience fields
+      innerRadius: (ext.config as any)?.inner_radius_mm,
+      nModes: (ext.config as any)?.n_modes,
+      subcases: (ext.config as any)?.subcases,
+      extractMethod: (ext.config as any)?.extract_method,
+    },
+  };
+}
+
+function makeObjectiveNode(obj: Objective, index: number): Node<ObjectiveNodeData> {
+  const pos = toCanvasPosition(
+    obj.canvas_position,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.objective, index)
+  );
+
+  return {
+    id: obj.id,
+    type: 'objective',
+    position: pos,
+    data: {
+      type: 'objective',
+      label: obj.name,
+      configured: Boolean(obj.source?.extractor_id && obj.source?.output_name),
+      name: obj.name,
+      direction: obj.direction,
+      weight: obj.weight,
+      extractorRef: obj.source?.extractor_id,
+      outputName: obj.source?.output_name,
+    },
+  };
+}
+
+function makeConstraintNode(con: Constraint, index: number): Node<ConstraintNodeData> {
+  const pos = toCanvasPosition(
+    con.canvas_position,
+    makeFallbackPosition(DEFAULT_LAYOUT.cols.constraint, index)
+  );
+
+  return {
+    id: con.id,
+    type: 'constraint',
+    position: pos,
+    data: {
+      type: 'constraint',
+      label: con.name,
+      configured: Boolean(con.source?.extractor_id && con.source?.output_name),
+      name: con.name,
+      operator: con.operator,
+      value: con.threshold,
+    },
+  };
+}
+
+// ---------------------------------------------------------------------------
+// Public API
+// ---------------------------------------------------------------------------
+
+export function specToNodes(spec: AtomizerSpec | null | undefined): Node<CanvasNodeData>[] {
+  if (!spec) return [];
+
+  const nodes: Node<CanvasNodeData>[] = [];
+
+  // Structural nodes
+  nodes.push(makeModelNode(spec) as Node<CanvasNodeData>);
+  nodes.push(makeSolverNode(spec) as Node<CanvasNodeData>);
+  nodes.push(makeAlgorithmNode(spec) as Node<CanvasNodeData>);
+  nodes.push(makeSurrogateNode(spec) as Node<CanvasNodeData>);
+
+  // Array nodes
+  spec.design_variables?.forEach((dv, i) => nodes.push(makeDesignVarNode(dv, i) as Node<CanvasNodeData>));
+  spec.extractors?.forEach((ext, i) => nodes.push(makeExtractorNode(ext, i) as Node<CanvasNodeData>));
+  spec.objectives?.forEach((obj, i) => nodes.push(makeObjectiveNode(obj, i) as Node<CanvasNodeData>));
+  spec.constraints?.forEach((con, i) => nodes.push(makeConstraintNode(con, i) as Node<CanvasNodeData>));
+
+  return nodes;
+}
+
+export function specToEdges(spec: AtomizerSpec | null | undefined): Edge[] {
+  if (!spec) return [];
+
+  const edges: Edge[] = [];
+  const seen = new Set<string>();
+
+  const add = (source: string, target: string, sourceHandle?: string, targetHandle?: string) => {
+    const id = `${source}__${target}${sourceHandle ? `__${sourceHandle}` : ''}${targetHandle ? `__${targetHandle}` : ''}`;
+    if (seen.has(id)) return;
+    seen.add(id);
+    edges.push({
+      id,
+      source,
+      target,
+      sourceHandle,
+      targetHandle,
+    });
+  };
+
+  // Prefer explicit canvas edges if present
+  if (spec.canvas?.edges && spec.canvas.edges.length > 0) {
+    for (const e of spec.canvas.edges) {
+      add(e.source, e.target, e.sourceHandle, e.targetHandle);
+    }
+    return edges;
+  }
+
+  // Fallback: build a minimal visual graph from spec fields (deterministic)
+  // DV → model
+  for (const dv of spec.design_variables || []) add(dv.id, 'model');
+  // model → solver
+  add('model', 'solver');
+  // solver → each extractor
+  for (const ext of spec.extractors || []) add('solver', ext.id);
+  // extractor → objective/constraint based on source
+  for (const obj of spec.objectives || []) {
+    if (obj.source?.extractor_id) add(obj.source.extractor_id, obj.id);
+  }
+  for (const con of spec.constraints || []) {
+    if (con.source?.extractor_id) add(con.source.extractor_id, con.id);
+  }
+  // objective/constraint → algorithm
+  for (const obj of spec.objectives || []) add(obj.id, 'algorithm');
+  for (const con of spec.constraints || []) add(con.id, 'algorithm');
+  // algorithm → surrogate
+  add('algorithm', 'surrogate');
+
+  return edges;
+}

atomizer-dashboard/frontend/src/lib/validation/specValidator.ts

@@ -0,0 +1,399 @@
+/**
+ * Spec Validator - Validate AtomizerSpec v2.0 before running optimization
+ * 
+ * This validator checks the spec for completeness and correctness,
+ * returning structured errors that can be displayed in the ValidationPanel.
+ */
+
+import { AtomizerSpec } from '../../types/atomizer-spec';
+import { ValidationError, ValidationData } from '../../hooks/usePanelStore';
+
+// ============================================================================
+// Validation Rules
+// ============================================================================
+
+interface ValidationRule {
+  code: string;
+  check: (spec: AtomizerSpec) => ValidationError | null;
+}
+
+const validationRules: ValidationRule[] = [
+  // ---- Critical Errors (must fix) ----
+  
+  {
+    code: 'NO_DESIGN_VARS',
+    check: (spec) => {
+      const enabledDVs = spec.design_variables.filter(dv => dv.enabled !== false);
+      if (enabledDVs.length === 0) {
+        return {
+          code: 'NO_DESIGN_VARS',
+          severity: 'error',
+          path: 'design_variables',
+          message: 'No design variables defined',
+          suggestion: 'Add at least one design variable from the introspection panel or drag from the palette.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'NO_OBJECTIVES',
+    check: (spec) => {
+      if (spec.objectives.length === 0) {
+        return {
+          code: 'NO_OBJECTIVES',
+          severity: 'error',
+          path: 'objectives',
+          message: 'No objectives defined',
+          suggestion: 'Add at least one objective to define what to optimize (minimize mass, maximize stiffness, etc.).',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'NO_EXTRACTORS',
+    check: (spec) => {
+      if (spec.extractors.length === 0) {
+        return {
+          code: 'NO_EXTRACTORS',
+          severity: 'error',
+          path: 'extractors',
+          message: 'No extractors defined',
+          suggestion: 'Add extractors to pull physics values (displacement, stress, frequency) from FEA results.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'NO_MODEL',
+    check: (spec) => {
+      if (!spec.model.sim?.path) {
+        return {
+          code: 'NO_MODEL',
+          severity: 'error',
+          path: 'model.sim.path',
+          message: 'No simulation file configured',
+          suggestion: 'Select a .sim file in the study\'s model directory.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  // ---- Design Variable Validation ----
+  
+  {
+    code: 'DV_INVALID_BOUNDS',
+    check: (spec) => {
+      for (const dv of spec.design_variables) {
+        if (dv.enabled === false) continue;
+        if (dv.bounds.min >= dv.bounds.max) {
+          return {
+            code: 'DV_INVALID_BOUNDS',
+            severity: 'error',
+            path: `design_variables.${dv.id}`,
+            message: `Design variable "${dv.name}" has invalid bounds (min >= max)`,
+            suggestion: `Set min (${dv.bounds.min}) to be less than max (${dv.bounds.max}).`,
+            nodeId: dv.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'DV_NO_EXPRESSION',
+    check: (spec) => {
+      for (const dv of spec.design_variables) {
+        if (dv.enabled === false) continue;
+        if (!dv.expression_name || dv.expression_name.trim() === '') {
+          return {
+            code: 'DV_NO_EXPRESSION',
+            severity: 'error',
+            path: `design_variables.${dv.id}`,
+            message: `Design variable "${dv.name}" has no NX expression name`,
+            suggestion: 'Set the expression_name to match an NX expression in the model.',
+            nodeId: dv.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  // ---- Extractor Validation ----
+  
+  {
+    code: 'EXTRACTOR_NO_TYPE',
+    check: (spec) => {
+      for (const ext of spec.extractors) {
+        if (!ext.type || ext.type.trim() === '') {
+          return {
+            code: 'EXTRACTOR_NO_TYPE',
+            severity: 'error',
+            path: `extractors.${ext.id}`,
+            message: `Extractor "${ext.name}" has no type selected`,
+            suggestion: 'Select an extractor type (displacement, stress, frequency, etc.).',
+            nodeId: ext.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'CUSTOM_EXTRACTOR_NO_CODE',
+    check: (spec) => {
+      for (const ext of spec.extractors) {
+        if (ext.type === 'custom_function' && (!ext.function?.source_code || ext.function.source_code.trim() === '')) {
+          return {
+            code: 'CUSTOM_EXTRACTOR_NO_CODE',
+            severity: 'error',
+            path: `extractors.${ext.id}`,
+            message: `Custom extractor "${ext.name}" has no code defined`,
+            suggestion: 'Open the code editor and write the extraction function.',
+            nodeId: ext.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  // ---- Objective Validation ----
+  
+  {
+    code: 'OBJECTIVE_NO_SOURCE',
+    check: (spec) => {
+      for (const obj of spec.objectives) {
+        // Check if objective is connected to an extractor via canvas edges
+        const hasSource = spec.canvas?.edges?.some(
+          edge => edge.target === obj.id && edge.source.startsWith('ext_')
+        );
+        
+        // Also check if source.extractor_id is set (and not UNSET placeholders)
+        const extractorId = obj.source?.extractor_id;
+        const outputName = obj.source?.output_name;
+        const hasDirectSource = Boolean(extractorId) &&
+          extractorId !== '__UNSET__' &&
+          Boolean(outputName) &&
+          outputName !== '__UNSET__' &&
+          spec.extractors.some(e => e.id === extractorId);
+        
+        if (!hasSource && !hasDirectSource) {
+          return {
+            code: 'OBJECTIVE_NO_SOURCE',
+            severity: 'error',
+            path: `objectives.${obj.id}`,
+            message: `Objective "${obj.name}" has no connected extractor`,
+            suggestion: 'Connect an extractor to this objective or set source_extractor_id.',
+            nodeId: obj.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  // ---- Constraint Validation ----
+  
+  {
+    code: 'CONSTRAINT_NO_THRESHOLD',
+    check: (spec) => {
+      for (const con of spec.constraints || []) {
+        if (con.threshold === undefined || con.threshold === null) {
+          return {
+            code: 'CONSTRAINT_NO_THRESHOLD',
+            severity: 'error',
+            path: `constraints.${con.id}`,
+            message: `Constraint "${con.name}" has no threshold value`,
+            suggestion: 'Set a threshold value for the constraint.',
+            nodeId: con.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  // ---- Warnings (can proceed but risky) ----
+  
+  {
+    code: 'HIGH_TRIAL_COUNT',
+    check: (spec) => {
+      const maxTrials = spec.optimization.budget?.max_trials || 100;
+      if (maxTrials > 500) {
+        return {
+          code: 'HIGH_TRIAL_COUNT',
+          severity: 'warning',
+          path: 'optimization.budget.max_trials',
+          message: `High trial count (${maxTrials}) may take several hours to complete`,
+          suggestion: 'Consider starting with fewer trials (50-100) to validate the setup.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'SINGLE_TRIAL',
+    check: (spec) => {
+      const maxTrials = spec.optimization.budget?.max_trials || 100;
+      if (maxTrials === 1) {
+        return {
+          code: 'SINGLE_TRIAL',
+          severity: 'warning',
+          path: 'optimization.budget.max_trials',
+          message: 'Only 1 trial configured - this will just run a single evaluation',
+          suggestion: 'Increase max_trials to explore the design space.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'DV_NARROW_BOUNDS',
+    check: (spec) => {
+      for (const dv of spec.design_variables) {
+        if (dv.enabled === false) continue;
+        const range = dv.bounds.max - dv.bounds.min;
+        const baseline = dv.baseline || (dv.bounds.min + dv.bounds.max) / 2;
+        const relativeRange = range / Math.abs(baseline || 1);
+        
+        if (relativeRange < 0.01) { // Less than 1% variation
+          return {
+            code: 'DV_NARROW_BOUNDS',
+            severity: 'warning',
+            path: `design_variables.${dv.id}`,
+            message: `Design variable "${dv.name}" has very narrow bounds (<1% range)`,
+            suggestion: 'Consider widening the bounds for more meaningful exploration.',
+            nodeId: dv.id,
+          };
+        }
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'MANY_DESIGN_VARS',
+    check: (spec) => {
+      const enabledDVs = spec.design_variables.filter(dv => dv.enabled !== false);
+      if (enabledDVs.length > 10) {
+        return {
+          code: 'MANY_DESIGN_VARS',
+          severity: 'warning',
+          path: 'design_variables',
+          message: `${enabledDVs.length} design variables - high-dimensional space may need more trials`,
+          suggestion: 'Consider enabling neural surrogate acceleration or increasing trial budget.',
+        };
+      }
+      return null;
+    },
+  },
+  
+  {
+    code: 'MULTI_OBJECTIVE_NO_WEIGHTS',
+    check: (spec) => {
+      if (spec.objectives.length > 1) {
+        const hasWeights = spec.objectives.every(obj => obj.weight !== undefined && obj.weight !== null);
+        if (!hasWeights) {
+          return {
+            code: 'MULTI_OBJECTIVE_NO_WEIGHTS',
+            severity: 'warning',
+            path: 'objectives',
+            message: 'Multi-objective optimization without explicit weights',
+            suggestion: 'Consider setting weights to control the trade-off between objectives.',
+          };
+        }
+      }
+      return null;
+    },
+  },
+];
+
+// ============================================================================
+// Main Validation Function
+// ============================================================================
+
+export function validateSpec(spec: AtomizerSpec): ValidationData {
+  const errors: ValidationError[] = [];
+  const warnings: ValidationError[] = [];
+  
+  for (const rule of validationRules) {
+    const result = rule.check(spec);
+    if (result) {
+      if (result.severity === 'error') {
+        errors.push(result);
+      } else {
+        warnings.push(result);
+      }
+    }
+  }
+  
+  return {
+    valid: errors.length === 0,
+    errors,
+    warnings,
+    checkedAt: Date.now(),
+  };
+}
+
+// ============================================================================
+// Quick Validation (just checks if can run)
+// ============================================================================
+
+export function canRunOptimization(spec: AtomizerSpec): { canRun: boolean; reason?: string } {
+  // Check critical requirements only
+  if (!spec.model.sim?.path) {
+    return { canRun: false, reason: 'No simulation file configured' };
+  }
+  
+  const enabledDVs = spec.design_variables.filter(dv => dv.enabled !== false);
+  if (enabledDVs.length === 0) {
+    return { canRun: false, reason: 'No design variables defined' };
+  }
+  
+  if (spec.objectives.length === 0) {
+    return { canRun: false, reason: 'No objectives defined' };
+  }
+  
+  if (spec.extractors.length === 0) {
+    return { canRun: false, reason: 'No extractors defined' };
+  }
+  
+  // Check for invalid bounds
+  for (const dv of enabledDVs) {
+    if (dv.bounds.min >= dv.bounds.max) {
+      return { canRun: false, reason: `Invalid bounds for "${dv.name}"` };
+    }
+  }
+  
+  return { canRun: true };
+}
+
+// ============================================================================
+// Export validation result type for backward compatibility
+// ============================================================================
+
+export interface LegacyValidationResult {
+  valid: boolean;
+  errors: string[];
+  warnings: string[];
+}
+
+export function toLegacyValidationResult(data: ValidationData): LegacyValidationResult {
+  return {
+    valid: data.valid,
+    errors: data.errors.map(e => e.message),
+    warnings: data.warnings.map(w => w.message),
+  };
+}

atomizer-dashboard/frontend/src/pages/CanvasView.tsx

@@ -10,9 +10,14 @@ import { ConfigImporter } from '../components/canvas/panels/ConfigImporter';
 import { NodeConfigPanel } from '../components/canvas/panels/NodeConfigPanel';
 import { NodeConfigPanelV2 } from '../components/canvas/panels/NodeConfigPanelV2';
 import { ChatPanel } from '../components/canvas/panels/ChatPanel';
+import { PanelContainer } from '../components/canvas/panels/PanelContainer';
+import { ResizeHandle } from '../components/canvas/ResizeHandle';
 import { useCanvasStore } from '../hooks/useCanvasStore';
-import { useSpecStore, useSpec, useSpecLoading, useSpecIsDirty, useSelectedNodeId } from '../hooks/useSpecStore';
+import { useSpecStore, useSpec, useSpecLoading, useSpecIsDirty, useSelectedNodeId, useSpecHash } from '../hooks/useSpecStore';
+import { useResizablePanel } from '../hooks/useResizablePanel';
+// usePanelStore is now used by child components - PanelContainer handles panels
 import { useSpecUndoRedo, useUndoRedoKeyboard } from '../hooks/useSpecUndoRedo';
+import { useSpecDraft } from '../hooks/useSpecDraft';
 import { useStudy } from '../context/StudyContext';
 import { useChat } from '../hooks/useChat';
 import { CanvasTemplate } from '../lib/canvas/templates';
@@ -29,6 +34,23 @@ export function CanvasView() {
   const [paletteCollapsed, setPaletteCollapsed] = useState(false);
   const [leftSidebarTab, setLeftSidebarTab] = useState<'components' | 'files'>('components');
   const navigate = useNavigate();
+
+  // Resizable panels
+  const leftPanel = useResizablePanel({
+    storageKey: 'left-sidebar',
+    defaultWidth: 240,
+    minWidth: 200,
+    maxWidth: 400,
+    side: 'left',
+  });
+
+  const rightPanel = useResizablePanel({
+    storageKey: 'right-panel',
+    defaultWidth: 384,
+    minWidth: 280,
+    maxWidth: 600,
+    side: 'right',
+  });
   const [searchParams] = useSearchParams();
 
   // Spec mode is the default (AtomizerSpec v2.0)
@@ -42,6 +64,10 @@ export function CanvasView() {
   // Get study ID from URL params (supports nested paths like M1_Mirror/study_name)
   const { '*': urlStudyId } = useParams<{ '*': string }>();
 
+  // Active study ID comes ONLY from URL - don't auto-load from context
+  // This ensures /canvas shows empty canvas, /canvas/{id} shows the study
+  const activeStudyId = urlStudyId;
+
   // Legacy canvas store (for backwards compatibility)
   const { nodes, edges, clear, loadFromConfig, toIntent } = useCanvasStore();
 
@@ -49,11 +75,22 @@ export function CanvasView() {
   const spec = useSpec();
   const specLoading = useSpecLoading();
   const specIsDirty = useSpecIsDirty();
+  const specHash = useSpecHash();
   const selectedNodeId = useSelectedNodeId();
   const { loadSpec, saveSpec, reloadSpec } = useSpecStore();
 
+  // S2: local autosave draft (crash-proof) — publish remains explicit
+  const { hasDraft, draft, discardDraft, reloadDraft } = useSpecDraft({
+    studyId: activeStudyId,
+    spec,
+    serverHash: specHash,
+    enabled: useSpecMode,
+  });
+
+  const [showDraftPrompt, setShowDraftPrompt] = useState(false);
+
   const { setSelectedStudy, studies } = useStudy();
-  const { clearSpec, setSpecFromWebSocket } = useSpecStore();
+  const { clearSpec, setSpecFromWebSocket, setSpecLocalDraft } = useSpecStore();
 
   // Undo/Redo for spec mode
   const undoRedo = useSpecUndoRedo();
@@ -62,10 +99,6 @@ export function CanvasView() {
   // Enable keyboard shortcuts for undo/redo (Ctrl+Z, Ctrl+Y)
   useUndoRedoKeyboard(undoRedo);
 
-  // Active study ID comes ONLY from URL - don't auto-load from context
-  // This ensures /canvas shows empty canvas, /canvas/{id} shows the study
-  const activeStudyId = urlStudyId;
-
   // Chat hook for assistant panel
   const { messages, isThinking, isConnected, sendMessage, notifyCanvasEdit } = useChat({
     studyId: activeStudyId,
@@ -109,6 +142,18 @@ export function CanvasView() {
     }
   }, [urlStudyId, useSpecMode]);
 
+  // If a local draft exists for this study, prompt user to restore/discard.
+  useEffect(() => {
+    if (!useSpecMode) return;
+    if (!activeStudyId) return;
+    if (specLoading) return;
+    if (!spec) return;
+    if (!hasDraft || !draft) return;
+
+    // Show prompt once per navigation
+    setShowDraftPrompt(true);
+  }, [useSpecMode, activeStudyId, specLoading, spec, hasDraft, draft]);
+
   // Notify Claude when user edits the spec (bi-directional sync)
   // This sends the updated spec to Claude so it knows what the user changed
   useEffect(() => {
@@ -162,7 +207,7 @@ export function CanvasView() {
       if (useSpecMode && spec) {
         // Save spec using new API
         await saveSpec(spec);
-        showNotification('Saved to atomizer_spec.json');
+        showNotification('Published to atomizer_spec.json');
       } else {
         // Legacy save
         const intent = toIntent();
@@ -296,17 +341,34 @@ export function CanvasView() {
 
         {/* Action Buttons */}
         <div className="flex items-center gap-2">
-          {/* Save Button - only show when there's a study and changes */}
-          {activeStudyId && (
+          {/* Save Button - always show in spec mode with study, grayed when no changes */}
+          {useSpecMode && spec && (
             <button
               onClick={saveToConfig}
-              disabled={isSaving || (useSpecMode ? !specIsDirty : !hasUnsavedChanges)}
+              disabled={isSaving || !specIsDirty}
               className={`px-3 py-1.5 text-sm rounded-lg transition-colors flex items-center gap-1.5 ${
-                (useSpecMode ? specIsDirty : hasUnsavedChanges)
+                specIsDirty
                   ? 'bg-green-600 hover:bg-green-500 text-white'
                   : 'bg-dark-700 text-dark-400 cursor-not-allowed border border-dark-600'
               }`}
-              title={(useSpecMode ? specIsDirty : hasUnsavedChanges) ? `Save changes to ${useSpecMode ? 'atomizer_spec.json' : 'optimization_config.json'}` : 'No changes to save'}
+              title={specIsDirty ? 'Publish draft to atomizer_spec.json' : 'No changes to publish'}
+            >
+              <Save size={14} />
+              {isSaving ? 'Publishing...' : 'Publish'}
+            </button>
+          )}
+          
+          {/* Legacy Save Button */}
+          {!useSpecMode && activeStudyId && (
+            <button
+              onClick={saveToConfig}
+              disabled={isSaving || !hasUnsavedChanges}
+              className={`px-3 py-1.5 text-sm rounded-lg transition-colors flex items-center gap-1.5 ${
+                hasUnsavedChanges
+                  ? 'bg-green-600 hover:bg-green-500 text-white'
+                  : 'bg-dark-700 text-dark-400 cursor-not-allowed border border-dark-600'
+              }`}
+              title={hasUnsavedChanges ? 'Save changes to optimization_config.json' : 'No changes to save'}
             >
               <Save size={14} />
               {isSaving ? 'Saving...' : 'Save'}
@@ -314,7 +376,7 @@ export function CanvasView() {
           )}
 
           {/* Reload Button */}
-          {activeStudyId && (
+          {(useSpecMode ? spec : activeStudyId) && (
             <button
               onClick={handleReload}
               disabled={isLoading || specLoading}
@@ -404,7 +466,10 @@ export function CanvasView() {
       <main className="flex-1 overflow-hidden flex">
         {/* Left Sidebar with tabs (spec mode only - AtomizerCanvas has its own) */}
         {useSpecMode && (
-          <div className={`${paletteCollapsed ? 'w-14' : 'w-60'} bg-dark-850 border-r border-dark-700 flex flex-col transition-all duration-200`}>
+          <div 
+            className="relative bg-dark-850 border-r border-dark-700 flex flex-col"
+            style={{ width: paletteCollapsed ? 56 : leftPanel.width }}
+          >
             {/* Tab buttons (only show when expanded) */}
             {!paletteCollapsed && (
               <div className="flex border-b border-dark-700">
@@ -450,6 +515,16 @@ export function CanvasView() {
                 />
               )}
             </div>
+
+            {/* Resize handle (only when not collapsed) */}
+            {!paletteCollapsed && (
+              <ResizeHandle
+                onMouseDown={leftPanel.startDrag}
+                onDoubleClick={leftPanel.resetWidth}
+                isDragging={leftPanel.isDragging}
+                position="right"
+              />
+            )}
           </div>
         )}
 
@@ -472,19 +547,38 @@ export function CanvasView() {
         </div>
 
         {/* Config Panel - use V2 for spec mode, legacy for AtomizerCanvas */}
-        {selectedNodeId && !showChat && (
+        {/* Shows INSTEAD of chat when a node is selected */}
+        {selectedNodeId ? (
           useSpecMode ? (
-            <NodeConfigPanelV2 onClose={() => useSpecStore.getState().clearSelection()} />
+            <div 
+              className="relative border-l border-dark-700 bg-dark-850 flex flex-col"
+              style={{ width: rightPanel.width }}
+            >
+              <ResizeHandle
+                onMouseDown={rightPanel.startDrag}
+                onDoubleClick={rightPanel.resetWidth}
+                isDragging={rightPanel.isDragging}
+                position="left"
+              />
+              <NodeConfigPanelV2 onClose={() => useSpecStore.getState().clearSelection()} />
+            </div>
           ) : (
             <div className="w-80 border-l border-dark-700 bg-dark-850 overflow-y-auto">
               <NodeConfigPanel nodeId={selectedNodeId} />
             </div>
           )
-        )}
-
-        {/* Chat/Assistant Panel */}
-        {showChat && (
-          <div className="w-96 border-l border-dark-700 bg-dark-850 flex flex-col">
+        ) : showChat ? (
+          <div 
+            className="relative border-l border-dark-700 bg-dark-850 flex flex-col"
+            style={{ width: rightPanel.width }}
+          >
+            {/* Resize handle */}
+            <ResizeHandle
+              onMouseDown={rightPanel.startDrag}
+              onDoubleClick={rightPanel.resetWidth}
+              isDragging={rightPanel.isDragging}
+              position="left"
+            />
             {/* Chat Header */}
             <div className="flex items-center justify-between px-4 py-3 border-b border-dark-700">
               <div className="flex items-center gap-2">
@@ -524,7 +618,7 @@ export function CanvasView() {
               isConnected={isConnected}
             />
           </div>
-        )}
+        ) : null}
       </main>
 
       {/* Template Selector Modal */}
@@ -541,6 +635,49 @@ export function CanvasView() {
         onImport={handleImport}
       />
 
+      {/* Floating Panels (Introspection, Validation, Error, Results) */}
+      {useSpecMode && <PanelContainer />}
+
+      {/* Draft Restore Prompt (S2) */}
+      {useSpecMode && showDraftPrompt && draft && (
+        <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/60 backdrop-blur-sm">
+          <div className="w-[640px] max-w-[92vw] bg-dark-850 rounded-xl border border-dark-600 shadow-2xl p-5">
+            <h3 className="text-lg font-semibold text-white">Restore local draft?</h3>
+            <p className="text-sm text-dark-300 mt-2">
+              A local draft was found for this study (autosaved). You can restore it (recommended) or discard it and keep the published version.
+            </p>
+
+            <div className="mt-4 p-3 bg-dark-900/40 border border-dark-700 rounded-lg text-xs text-dark-400">
+              <div>Draft updated: {new Date(draft.updatedAt).toLocaleString()}</div>
+              <div>Base hash: {draft.baseHash || '(unknown)'}</div>
+            </div>
+
+            <div className="mt-5 flex justify-end gap-2">
+              <button
+                onClick={() => {
+                  discardDraft();
+                  setShowDraftPrompt(false);
+                  showNotification('Discarded local draft');
+                }}
+                className="px-4 py-2 bg-dark-700 text-dark-200 hover:bg-dark-600 rounded-lg border border-dark-600 transition-colors"
+              >
+                Discard Draft
+              </button>
+              <button
+                onClick={() => {
+                  setSpecLocalDraft(draft.spec, activeStudyId || undefined);
+                  setShowDraftPrompt(false);
+                  showNotification('Restored local draft');
+                }}
+                className="px-4 py-2 bg-primary-600 text-white hover:bg-primary-500 rounded-lg border border-primary-500 transition-colors"
+              >
+                Restore Draft
+              </button>
+            </div>
+          </div>
+        </div>
+      )}
+
       {/* Notification Toast */}
       {notification && (
         <div

atomizer-dashboard/frontend/src/pages/Home.tsx

@@ -18,12 +18,15 @@ import {
   FolderOpen,
   Maximize2,
   X,
-  Layers
+  Layers,
+  Sparkles,
+  Settings2
 } from 'lucide-react';
 import { useStudy } from '../context/StudyContext';
 import { Study } from '../types';
 import { apiClient } from '../api/client';
 import { MarkdownRenderer } from '../components/MarkdownRenderer';
+import { InboxSection } from '../components/intake';
 
 const Home: React.FC = () => {
   const { studies, setSelectedStudy, refreshStudies, isLoading } = useStudy();
@@ -174,6 +177,18 @@ const Home: React.FC = () => {
               />
             </div>
             <div className="flex items-center gap-3">
+              <button
+                onClick={() => navigate('/studio')}
+                className="flex items-center gap-2 px-4 py-2 rounded-lg transition-all font-medium hover:-translate-y-0.5"
+                style={{
+                  background: 'linear-gradient(135deg, #f59e0b 0%, #d97706 100%)',
+                  color: '#000',
+                  boxShadow: '0 4px 15px rgba(245, 158, 11, 0.3)'
+                }}
+              >
+                <Sparkles className="w-4 h-4" />
+                New Study
+              </button>
               <button
                 onClick={() => navigate('/canvas')}
                 className="flex items-center gap-2 px-4 py-2 rounded-lg transition-all font-medium hover:-translate-y-0.5"
@@ -250,6 +265,11 @@ const Home: React.FC = () => {
           </div>
         </div>
 
+        {/* Inbox Section - Study Creation Workflow */}
+        <div className="mb-8">
+          <InboxSection onStudyFinalized={refreshStudies} />
+        </div>
+
         {/* Two-column layout: Table + Preview */}
         <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
           {/* Study Table */}
@@ -407,6 +427,19 @@ const Home: React.FC = () => {
                       <Layers className="w-4 h-4" />
                       Canvas
                     </button>
+                    <button
+                      onClick={() => navigate(`/studio/${selectedPreview.id}`)}
+                      className="flex items-center gap-2 px-4 py-2.5 rounded-lg transition-all font-medium whitespace-nowrap hover:-translate-y-0.5"
+                      style={{
+                        background: 'rgba(8, 15, 26, 0.85)',
+                        border: '1px solid rgba(245, 158, 11, 0.3)',
+                        color: '#f59e0b'
+                      }}
+                      title="Edit study configuration with AI assistant"
+                    >
+                      <Settings2 className="w-4 h-4" />
+                      Studio
+                    </button>
                     <button
                       onClick={() => handleSelectStudy(selectedPreview)}
                       className="flex items-center gap-2 px-5 py-2.5 rounded-lg transition-all font-semibold whitespace-nowrap hover:-translate-y-0.5"

atomizer-dashboard/frontend/src/pages/Studio.tsx

@@ -0,0 +1,672 @@
+/**
+ * Atomizer Studio - Unified Study Creation Environment
+ * 
+ * A drag-and-drop workspace for creating optimization studies with:
+ * - File upload (models + context documents)
+ * - Visual canvas configuration
+ * - AI-powered assistance
+ * - One-click build to final study
+ */
+
+import { useState, useEffect, useCallback, useRef } from 'react';
+import { useNavigate, useParams } from 'react-router-dom';
+import {
+  Home,
+  ChevronRight,
+  Upload,
+  FileText,
+  Settings,
+  Sparkles,
+  Save,
+  RefreshCw,
+  Trash2,
+  MessageSquare,
+  Layers,
+  CheckCircle,
+  AlertCircle,
+  Loader2,
+  X,
+  ChevronLeft,
+  ChevronRight as ChevronRightIcon,
+  GripVertical,
+} from 'lucide-react';
+import { intakeApi } from '../api/intake';
+import { SpecRenderer } from '../components/canvas/SpecRenderer';
+import { NodePalette } from '../components/canvas/palette/NodePalette';
+import { NodeConfigPanelV2 } from '../components/canvas/panels/NodeConfigPanelV2';
+import { useSpecStore, useSpec, useSpecLoading } from '../hooks/useSpecStore';
+import { StudioDropZone } from '../components/studio/StudioDropZone';
+import { StudioParameterList } from '../components/studio/StudioParameterList';
+import { StudioContextFiles } from '../components/studio/StudioContextFiles';
+import { StudioChat } from '../components/studio/StudioChat';
+import { StudioBuildDialog } from '../components/studio/StudioBuildDialog';
+
+interface DraftState {
+  draftId: string | null;
+  status: 'idle' | 'creating' | 'ready' | 'error';
+  error: string | null;
+  modelFiles: string[];
+  contextFiles: string[];
+  contextContent: string;
+  introspectionAvailable: boolean;
+  designVariableCount: number;
+  objectiveCount: number;
+}
+
+export default function Studio() {
+  const navigate = useNavigate();
+  const { draftId: urlDraftId } = useParams<{ draftId: string }>();
+  
+  // Draft state
+  const [draft, setDraft] = useState<DraftState>({
+    draftId: null,
+    status: 'idle',
+    error: null,
+    modelFiles: [],
+    contextFiles: [],
+    contextContent: '',
+    introspectionAvailable: false,
+    designVariableCount: 0,
+    objectiveCount: 0,
+  });
+  
+  // UI state
+  const [leftPanelWidth, setLeftPanelWidth] = useState(320);
+  const [rightPanelCollapsed, setRightPanelCollapsed] = useState(false);
+  const [showBuildDialog, setShowBuildDialog] = useState(false);
+  const [isIntrospecting, setIsIntrospecting] = useState(false);
+  const [notification, setNotification] = useState<{ type: 'success' | 'error' | 'info'; message: string } | null>(null);
+  
+  // Resize state
+  const isResizing = useRef(false);
+  const minPanelWidth = 280;
+  const maxPanelWidth = 500;
+  
+  // Spec store for canvas
+  const spec = useSpec();
+  const specLoading = useSpecLoading();
+  const { loadSpec, clearSpec } = useSpecStore();
+  
+  // Handle panel resize
+  const handleMouseDown = useCallback((e: React.MouseEvent) => {
+    e.preventDefault();
+    isResizing.current = true;
+    document.body.style.cursor = 'col-resize';
+    document.body.style.userSelect = 'none';
+  }, []);
+  
+  useEffect(() => {
+    const handleMouseMove = (e: MouseEvent) => {
+      if (!isResizing.current) return;
+      const newWidth = Math.min(maxPanelWidth, Math.max(minPanelWidth, e.clientX));
+      setLeftPanelWidth(newWidth);
+    };
+    
+    const handleMouseUp = () => {
+      isResizing.current = false;
+      document.body.style.cursor = '';
+      document.body.style.userSelect = '';
+    };
+    
+    document.addEventListener('mousemove', handleMouseMove);
+    document.addEventListener('mouseup', handleMouseUp);
+    
+    return () => {
+      document.removeEventListener('mousemove', handleMouseMove);
+      document.removeEventListener('mouseup', handleMouseUp);
+    };
+  }, []);
+  
+  // Initialize or load draft on mount
+  useEffect(() => {
+    if (urlDraftId) {
+      loadDraft(urlDraftId);
+    } else {
+      createNewDraft();
+    }
+    
+    return () => {
+      // Cleanup: clear spec when leaving Studio
+      clearSpec();
+    };
+  }, [urlDraftId]);
+  
+  // Create a new draft
+  const createNewDraft = async () => {
+    setDraft(prev => ({ ...prev, status: 'creating', error: null }));
+    
+    try {
+      const response = await intakeApi.createDraft();
+      
+      setDraft({
+        draftId: response.draft_id,
+        status: 'ready',
+        error: null,
+        modelFiles: [],
+        contextFiles: [],
+        contextContent: '',
+        introspectionAvailable: false,
+        designVariableCount: 0,
+        objectiveCount: 0,
+      });
+      
+      // Update URL without navigation
+      window.history.replaceState(null, '', `/studio/${response.draft_id}`);
+      
+      // Load the empty spec for this draft
+      await loadSpec(response.draft_id);
+      
+      showNotification('info', 'New studio session started. Drop your files to begin.');
+    } catch (err) {
+      setDraft(prev => ({
+        ...prev,
+        status: 'error',
+        error: err instanceof Error ? err.message : 'Failed to create draft',
+      }));
+    }
+  };
+  
+  // Load existing draft or study
+  const loadDraft = async (studyId: string) => {
+    setDraft(prev => ({ ...prev, status: 'creating', error: null }));
+    
+    // Check if this is a draft (in _inbox) or an existing study
+    const isDraft = studyId.startsWith('draft_');
+    
+    if (isDraft) {
+      // Load from intake API
+      try {
+        const response = await intakeApi.getStudioDraft(studyId);
+        
+        // Also load context content if there are context files
+        let contextContent = '';
+        if (response.context_files.length > 0) {
+          try {
+            const contextResponse = await intakeApi.getContextContent(studyId);
+            contextContent = contextResponse.content;
+          } catch {
+            // Ignore context loading errors
+          }
+        }
+        
+        setDraft({
+          draftId: response.draft_id,
+          status: 'ready',
+          error: null,
+          modelFiles: response.model_files,
+          contextFiles: response.context_files,
+          contextContent,
+          introspectionAvailable: response.introspection_available,
+          designVariableCount: response.design_variable_count,
+          objectiveCount: response.objective_count,
+        });
+        
+        // Load the spec
+        await loadSpec(studyId);
+        
+        showNotification('info', `Resuming draft: ${studyId}`);
+      } catch (err) {
+        // Draft doesn't exist, create new one
+        createNewDraft();
+      }
+    } else {
+      // Load existing study directly via spec store
+      try {
+        await loadSpec(studyId);
+        
+        // Get counts from loaded spec
+        const loadedSpec = useSpecStore.getState().spec;
+        
+        setDraft({
+          draftId: studyId,
+          status: 'ready',
+          error: null,
+          modelFiles: [], // Existing studies don't track files separately
+          contextFiles: [],
+          contextContent: '',
+          introspectionAvailable: true, // Assume introspection was done
+          designVariableCount: loadedSpec?.design_variables?.length || 0,
+          objectiveCount: loadedSpec?.objectives?.length || 0,
+        });
+        
+        showNotification('info', `Editing study: ${studyId}`);
+      } catch (err) {
+        setDraft(prev => ({
+          ...prev,
+          status: 'error',
+          error: err instanceof Error ? err.message : 'Failed to load study',
+        }));
+      }
+    }
+  };
+  
+  // Refresh draft data
+  const refreshDraft = async () => {
+    if (!draft.draftId) return;
+    
+    const isDraft = draft.draftId.startsWith('draft_');
+    
+    if (isDraft) {
+      try {
+        const response = await intakeApi.getStudioDraft(draft.draftId);
+        
+        // Also refresh context content
+        let contextContent = draft.contextContent;
+        if (response.context_files.length > 0) {
+          try {
+            const contextResponse = await intakeApi.getContextContent(draft.draftId);
+            contextContent = contextResponse.content;
+          } catch {
+            // Keep existing content
+          }
+        }
+        
+        setDraft(prev => ({
+          ...prev,
+          modelFiles: response.model_files,
+          contextFiles: response.context_files,
+          contextContent,
+          introspectionAvailable: response.introspection_available,
+          designVariableCount: response.design_variable_count,
+          objectiveCount: response.objective_count,
+        }));
+        
+        // Reload spec
+        await loadSpec(draft.draftId);
+      } catch (err) {
+        showNotification('error', 'Failed to refresh draft');
+      }
+    } else {
+      // For existing studies, just reload the spec
+      try {
+        await loadSpec(draft.draftId);
+        
+        const loadedSpec = useSpecStore.getState().spec;
+        setDraft(prev => ({
+          ...prev,
+          designVariableCount: loadedSpec?.design_variables?.length || 0,
+          objectiveCount: loadedSpec?.objectives?.length || 0,
+        }));
+      } catch (err) {
+        showNotification('error', 'Failed to refresh study');
+      }
+    }
+  };
+  
+  // Run introspection
+  const runIntrospection = async () => {
+    if (!draft.draftId || draft.modelFiles.length === 0) {
+      showNotification('error', 'Please upload model files first');
+      return;
+    }
+    
+    setIsIntrospecting(true);
+    
+    try {
+      const response = await intakeApi.introspect({ study_name: draft.draftId });
+      
+      showNotification('success', `Found ${response.expressions_count} expressions (${response.candidates_count} candidates)`);
+      
+      // Refresh draft state
+      await refreshDraft();
+    } catch (err) {
+      showNotification('error', err instanceof Error ? err.message : 'Introspection failed');
+    } finally {
+      setIsIntrospecting(false);
+    }
+  };
+  
+  // Handle file upload complete
+  const handleUploadComplete = useCallback(() => {
+    refreshDraft();
+    showNotification('success', 'Files uploaded successfully');
+  }, [draft.draftId]);
+  
+  // Handle build complete
+  const handleBuildComplete = (finalPath: string, finalName: string) => {
+    setShowBuildDialog(false);
+    showNotification('success', `Study "${finalName}" created successfully!`);
+    
+    // Navigate to the new study
+    setTimeout(() => {
+      navigate(`/canvas/${finalPath.replace('studies/', '')}`);
+    }, 1500);
+  };
+  
+  // Reset draft
+  const resetDraft = async () => {
+    if (!draft.draftId) return;
+    
+    if (!confirm('Are you sure you want to reset? This will delete all uploaded files and configurations.')) {
+      return;
+    }
+    
+    try {
+      await intakeApi.deleteInboxStudy(draft.draftId);
+      await createNewDraft();
+    } catch (err) {
+      showNotification('error', 'Failed to reset draft');
+    }
+  };
+  
+  // Show notification
+  const showNotification = (type: 'success' | 'error' | 'info', message: string) => {
+    setNotification({ type, message });
+    setTimeout(() => setNotification(null), 4000);
+  };
+  
+  // Can always save/build - even empty studies can be saved for later
+  const canBuild = draft.draftId !== null;
+  
+  // Loading state
+  if (draft.status === 'creating') {
+    return (
+      <div className="min-h-screen bg-dark-900 flex items-center justify-center">
+        <div className="text-center">
+          <Loader2 className="w-12 h-12 text-primary-400 animate-spin mx-auto mb-4" />
+          <p className="text-dark-300">Initializing Studio...</p>
+        </div>
+      </div>
+    );
+  }
+  
+  // Error state
+  if (draft.status === 'error') {
+    return (
+      <div className="min-h-screen bg-dark-900 flex items-center justify-center">
+        <div className="text-center max-w-md">
+          <AlertCircle className="w-12 h-12 text-red-400 mx-auto mb-4" />
+          <h2 className="text-xl font-semibold text-white mb-2">Failed to Initialize</h2>
+          <p className="text-dark-400 mb-4">{draft.error}</p>
+          <button
+            onClick={createNewDraft}
+            className="px-4 py-2 bg-primary-500 text-white rounded-lg hover:bg-primary-400 transition-colors"
+          >
+            Try Again
+          </button>
+        </div>
+      </div>
+    );
+  }
+  
+  return (
+    <div className="min-h-screen bg-dark-900 flex flex-col">
+      {/* Header */}
+      <header className="h-14 bg-dark-850 border-b border-dark-700 flex items-center justify-between px-4 flex-shrink-0">
+        {/* Left: Navigation */}
+        <div className="flex items-center gap-3">
+          <button
+            onClick={() => navigate('/')}
+            className="p-2 hover:bg-dark-700 rounded-lg text-dark-400 hover:text-white transition-colors"
+          >
+            <Home className="w-5 h-5" />
+          </button>
+          <ChevronRight className="w-4 h-4 text-dark-600" />
+          <div className="flex items-center gap-2">
+            <Sparkles className="w-5 h-5 text-primary-400" />
+            <span className="text-white font-medium">Atomizer Studio</span>
+          </div>
+          {draft.draftId && (
+            <>
+              <ChevronRight className="w-4 h-4 text-dark-600" />
+              <span className="text-dark-400 text-sm font-mono">{draft.draftId}</span>
+            </>
+          )}
+        </div>
+        
+        {/* Right: Actions */}
+        <div className="flex items-center gap-2">
+          <button
+            onClick={resetDraft}
+            className="flex items-center gap-2 px-3 py-1.5 text-sm text-dark-400 hover:text-white hover:bg-dark-700 rounded-lg transition-colors"
+          >
+            <Trash2 className="w-4 h-4" />
+            Reset
+          </button>
+          <button
+            onClick={() => setShowBuildDialog(true)}
+            disabled={!canBuild}
+            className="flex items-center gap-2 px-4 py-1.5 text-sm font-medium bg-primary-500 text-white rounded-lg hover:bg-primary-400 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
+          >
+            <Save className="w-4 h-4" />
+            Save & Name Study
+          </button>
+        </div>
+      </header>
+      
+      {/* Main Content */}
+      <div className="flex-1 flex overflow-hidden">
+        {/* Left Panel: Resources (Resizable) */}
+        <div
+          className="bg-dark-850 border-r border-dark-700 flex flex-col flex-shrink-0 relative"
+          style={{ width: leftPanelWidth }}
+        >
+          <div className="flex-1 overflow-y-auto p-4 space-y-6">
+            {/* Drop Zone */}
+            <section>
+              <h3 className="text-sm font-medium text-dark-300 mb-3 flex items-center gap-2">
+                <Upload className="w-4 h-4" />
+                Model Files
+              </h3>
+              {draft.draftId && (
+                <StudioDropZone
+                  draftId={draft.draftId}
+                  type="model"
+                  files={draft.modelFiles}
+                  onUploadComplete={handleUploadComplete}
+                />
+              )}
+            </section>
+            
+            {/* Introspection */}
+            {draft.modelFiles.length > 0 && (
+              <section>
+                <div className="flex items-center justify-between mb-3">
+                  <h3 className="text-sm font-medium text-dark-300 flex items-center gap-2">
+                    <Settings className="w-4 h-4" />
+                    Parameters
+                  </h3>
+                  <button
+                    onClick={runIntrospection}
+                    disabled={isIntrospecting}
+                    className="flex items-center gap-1 px-2 py-1 text-xs text-primary-400 hover:bg-primary-400/10 rounded transition-colors disabled:opacity-50"
+                  >
+                    {isIntrospecting ? (
+                      <Loader2 className="w-3 h-3 animate-spin" />
+                    ) : (
+                      <RefreshCw className="w-3 h-3" />
+                    )}
+                    {isIntrospecting ? 'Scanning...' : 'Scan'}
+                  </button>
+                </div>
+                {draft.draftId && draft.introspectionAvailable && (
+                  <StudioParameterList
+                    draftId={draft.draftId}
+                    onParameterAdded={refreshDraft}
+                  />
+                )}
+                {!draft.introspectionAvailable && (
+                  <p className="text-xs text-dark-500 italic">
+                    Click "Scan" to discover parameters from your model.
+                  </p>
+                )}
+              </section>
+            )}
+            
+            {/* Context Files */}
+            <section>
+              <h3 className="text-sm font-medium text-dark-300 mb-3 flex items-center gap-2">
+                <FileText className="w-4 h-4" />
+                Context Documents
+              </h3>
+              {draft.draftId && (
+                <StudioContextFiles
+                  draftId={draft.draftId}
+                  files={draft.contextFiles}
+                  onUploadComplete={handleUploadComplete}
+                />
+              )}
+              <p className="text-xs text-dark-500 mt-2">
+                Upload requirements, goals, or specs. The AI will read these.
+              </p>
+              
+              {/* Show context preview if loaded */}
+              {draft.contextContent && (
+                <div className="mt-3 p-2 bg-dark-700/50 rounded-lg border border-dark-600">
+                  <p className="text-xs text-amber-400 mb-1 font-medium">Context Loaded:</p>
+                  <p className="text-xs text-dark-400 line-clamp-3">
+                    {draft.contextContent.substring(0, 200)}...
+                  </p>
+                </div>
+              )}
+            </section>
+            
+            {/* Node Palette - EXPANDED, not collapsed */}
+            <section>
+              <h3 className="text-sm font-medium text-dark-300 mb-3 flex items-center gap-2">
+                <Layers className="w-4 h-4" />
+                Components
+              </h3>
+              <NodePalette 
+                collapsed={false} 
+                showToggle={false}
+                className="!w-full !border-0 !bg-transparent"
+              />
+            </section>
+          </div>
+          
+          {/* Resize Handle */}
+          <div
+            className="absolute right-0 top-0 bottom-0 w-1 cursor-col-resize hover:bg-primary-500/50 transition-colors group"
+            onMouseDown={handleMouseDown}
+          >
+            <div className="absolute right-0 top-1/2 -translate-y-1/2 w-4 h-8 flex items-center justify-center opacity-0 group-hover:opacity-100 transition-opacity">
+              <GripVertical className="w-3 h-3 text-dark-400" />
+            </div>
+          </div>
+        </div>
+        
+        {/* Center: Canvas */}
+        <div className="flex-1 relative bg-dark-900">
+          {draft.draftId && (
+            <SpecRenderer
+              studyId={draft.draftId}
+              editable={true}
+              showLoadingOverlay={false}
+            />
+          )}
+          
+          {/* Empty state */}
+          {!specLoading && (!spec || Object.keys(spec).length === 0) && (
+            <div className="absolute inset-0 flex items-center justify-center pointer-events-none">
+              <div className="text-center max-w-md p-8">
+                <div className="w-20 h-20 rounded-full bg-dark-800 flex items-center justify-center mx-auto mb-6">
+                  <Sparkles className="w-10 h-10 text-primary-400" />
+                </div>
+                <h2 className="text-2xl font-semibold text-white mb-3">
+                  Welcome to Atomizer Studio
+                </h2>
+                <p className="text-dark-400 mb-6">
+                  Drop your model files on the left, or drag components from the palette to start building your optimization study.
+                </p>
+                <div className="flex flex-col gap-2 text-sm text-dark-500">
+                  <div className="flex items-center gap-2">
+                    <CheckCircle className="w-4 h-4 text-green-400" />
+                    <span>Upload .sim, .prt, .fem files</span>
+                  </div>
+                  <div className="flex items-center gap-2">
+                    <CheckCircle className="w-4 h-4 text-green-400" />
+                    <span>Add context documents (PDF, MD, TXT)</span>
+                  </div>
+                  <div className="flex items-center gap-2">
+                    <CheckCircle className="w-4 h-4 text-green-400" />
+                    <span>Configure with AI assistance</span>
+                  </div>
+                </div>
+              </div>
+            </div>
+          )}
+        </div>
+        
+        {/* Right Panel: Assistant + Config - wider for better chat UX */}
+        <div
+          className={`bg-dark-850 border-l border-dark-700 flex flex-col transition-all duration-300 flex-shrink-0 ${
+            rightPanelCollapsed ? 'w-12' : 'w-[480px]'
+          }`}
+        >
+          {/* Collapse toggle */}
+          <button
+            onClick={() => setRightPanelCollapsed(!rightPanelCollapsed)}
+            className="absolute right-0 top-1/2 -translate-y-1/2 z-10 p-1 bg-dark-700 border border-dark-600 rounded-l-lg hover:bg-dark-600 transition-colors"
+            style={{ marginRight: rightPanelCollapsed ? '48px' : '480px' }}
+          >
+            {rightPanelCollapsed ? (
+              <ChevronLeft className="w-4 h-4 text-dark-400" />
+            ) : (
+              <ChevronRightIcon className="w-4 h-4 text-dark-400" />
+            )}
+          </button>
+          
+          {!rightPanelCollapsed && (
+            <div className="flex-1 flex flex-col overflow-hidden">
+              {/* Chat */}
+              <div className="flex-1 overflow-hidden">
+                {draft.draftId && (
+                  <StudioChat
+                    draftId={draft.draftId}
+                    contextFiles={draft.contextFiles}
+                    contextContent={draft.contextContent}
+                    modelFiles={draft.modelFiles}
+                    onSpecUpdated={refreshDraft}
+                  />
+                )}
+              </div>
+              
+              {/* Config Panel (when node selected) */}
+              <NodeConfigPanelV2 />
+            </div>
+          )}
+          
+          {rightPanelCollapsed && (
+            <div className="flex flex-col items-center py-4 gap-4">
+              <MessageSquare className="w-5 h-5 text-dark-400" />
+            </div>
+          )}
+        </div>
+      </div>
+      
+      {/* Notification Toast */}
+      {notification && (
+        <div
+          className={`fixed bottom-4 right-4 flex items-center gap-3 px-4 py-3 rounded-lg shadow-lg z-50 animate-slide-up ${
+            notification.type === 'success'
+              ? 'bg-green-500/10 border border-green-500/30 text-green-400'
+              : notification.type === 'error'
+              ? 'bg-red-500/10 border border-red-500/30 text-red-400'
+              : 'bg-primary-500/10 border border-primary-500/30 text-primary-400'
+          }`}
+        >
+          {notification.type === 'success' && <CheckCircle className="w-5 h-5" />}
+          {notification.type === 'error' && <AlertCircle className="w-5 h-5" />}
+          {notification.type === 'info' && <Sparkles className="w-5 h-5" />}
+          <span>{notification.message}</span>
+          <button
+            onClick={() => setNotification(null)}
+            className="p-1 hover:bg-white/10 rounded"
+          >
+            <X className="w-4 h-4" />
+          </button>
+        </div>
+      )}
+      
+      {/* Build Dialog */}
+      {showBuildDialog && draft.draftId && (
+        <StudioBuildDialog
+          draftId={draft.draftId}
+          onClose={() => setShowBuildDialog(false)}
+          onBuildComplete={handleBuildComplete}
+        />
+      )}
+    </div>
+  );
+}

atomizer-dashboard/frontend/src/types/atomizer-spec.ts

@@ -39,6 +39,10 @@ export interface SpecMeta {
   tags?: string[];
   /** Real-world engineering context */
   engineering_context?: string;
+  /** Current workflow status */
+  status?: 'draft' | 'introspected' | 'configured' | 'validated' | 'ready' | 'running' | 'completed' | 'failed';
+  /** Topic/folder for organization */
+  topic?: string;
 }
 
 // ============================================================================
@@ -64,6 +68,29 @@ export interface FemConfig {
 }
 
 export type SolverType = 'nastran' | 'NX_Nastran' | 'abaqus';
+
+/**
+ * SolverEngine - The actual solver software used for analysis
+ * - nxnastran: NX Nastran (built into Siemens NX)
+ * - mscnastran: MSC Nastran (external)
+ * - python: Custom Python script
+ * - abaqus: Abaqus (future)
+ * - ansys: ANSYS (future)
+ */
+export type SolverEngine = 'nxnastran' | 'mscnastran' | 'python' | 'abaqus' | 'ansys';
+
+/**
+ * NastranSolutionType - Common Nastran solution types
+ */
+export type NastranSolutionType = 
+  | 'SOL101'   // Linear Statics
+  | 'SOL103'   // Normal Modes
+  | 'SOL105'   // Buckling
+  | 'SOL106'   // Nonlinear Statics
+  | 'SOL111'   // Modal Frequency Response
+  | 'SOL112'   // Modal Transient Response
+  | 'SOL200';  // Design Optimization
+
 export type SubcaseType = 'static' | 'modal' | 'thermal' | 'buckling';
 
 export interface Subcase {
@@ -75,10 +102,14 @@ export interface Subcase {
 export interface SimConfig {
   /** Path to .sim file */
   path: string;
-  /** Solver type */
+  /** Solver type (legacy, use engine instead) */
   solver: SolverType;
+  /** Solver engine software */
+  engine?: SolverEngine;
   /** Solution type (e.g., SOL101) */
-  solution_type?: string;
+  solution_type?: NastranSolutionType | string;
+  /** Python script path (for python engine) */
+  script_path?: string;
   /** Defined subcases */
   subcases?: Subcase[];
 }
@@ -89,11 +120,40 @@ export interface NxSettings {
   auto_start_nx?: boolean;
 }
 
+export interface IntrospectionExpression {
+  name: string;
+  value: number | null;
+  units: string | null;
+  formula: string | null;
+  is_candidate: boolean;
+  confidence: number;
+}
+
+export interface IntrospectionData {
+  timestamp: string;
+  solver_type: string | null;
+  mass_kg: number | null;
+  volume_mm3: number | null;
+  expressions: IntrospectionExpression[];
+  warnings: string[];
+  baseline: {
+    timestamp: string;
+    solve_time_seconds: number;
+    mass_kg: number | null;
+    max_displacement_mm: number | null;
+    max_stress_mpa: number | null;
+    success: boolean;
+    error: string | null;
+  } | null;
+}
+
 export interface ModelConfig {
   nx_part?: NxPartConfig;
+  prt?: NxPartConfig;
   fem?: FemConfig;
-  sim: SimConfig;
+  sim?: SimConfig;
   nx_settings?: NxSettings;
+  introspection?: IntrospectionData;
 }
 
 // ============================================================================

atomizer-dashboard/frontend/src/types/intake.ts

@@ -0,0 +1,201 @@
+/**
+ * Intake Workflow TypeScript Types
+ * 
+ * Types for the study intake/creation workflow.
+ */
+
+// ============================================================================
+// Status Types
+// ============================================================================
+
+export type SpecStatus = 
+  | 'draft'
+  | 'introspected'
+  | 'configured'
+  | 'validated'
+  | 'ready'
+  | 'running'
+  | 'completed'
+  | 'failed';
+
+// ============================================================================
+// Expression/Introspection Types
+// ============================================================================
+
+export interface ExpressionInfo {
+  /** Expression name in NX */
+  name: string;
+  /** Current value */
+  value: number | null;
+  /** Physical units */
+  units: string | null;
+  /** Expression formula if any */
+  formula: string | null;
+  /** Whether this is a design variable candidate */
+  is_candidate: boolean;
+  /** Confidence that this is a DV (0-1) */
+  confidence: number;
+}
+
+export interface BaselineData {
+  /** When baseline was run */
+  timestamp: string;
+  /** How long the solve took */
+  solve_time_seconds: number;
+  /** Computed mass from BDF/FEM */
+  mass_kg: number | null;
+  /** Max displacement result */
+  max_displacement_mm: number | null;
+  /** Max von Mises stress */
+  max_stress_mpa: number | null;
+  /** Whether baseline solve succeeded */
+  success: boolean;
+  /** Error message if failed */
+  error: string | null;
+}
+
+export interface IntrospectionData {
+  /** When introspection was run */
+  timestamp: string;
+  /** Detected solver type */
+  solver_type: string | null;
+  /** Mass from expressions or properties */
+  mass_kg: number | null;
+  /** Volume from mass properties */
+  volume_mm3: number | null;
+  /** Discovered expressions */
+  expressions: ExpressionInfo[];
+  /** Baseline solve results */
+  baseline: BaselineData | null;
+  /** Warnings from introspection */
+  warnings: string[];
+}
+
+// ============================================================================
+// Request/Response Types
+// ============================================================================
+
+export interface CreateInboxRequest {
+  study_name: string;
+  description?: string;
+  topic?: string;
+}
+
+export interface CreateInboxResponse {
+  success: boolean;
+  study_name: string;
+  inbox_path: string;
+  spec_path: string;
+  status: SpecStatus;
+}
+
+export interface IntrospectRequest {
+  study_name: string;
+  model_file?: string;
+}
+
+export interface IntrospectResponse {
+  success: boolean;
+  study_name: string;
+  status: SpecStatus;
+  expressions_count: number;
+  candidates_count: number;
+  mass_kg: number | null;
+  warnings: string[];
+}
+
+export interface InboxStudy {
+  study_name: string;
+  status: SpecStatus;
+  description: string | null;
+  topic: string | null;
+  created: string | null;
+  modified: string | null;
+  model_files: string[];
+  has_context: boolean;
+}
+
+export interface ListInboxResponse {
+  studies: InboxStudy[];
+  total: number;
+}
+
+export interface TopicInfo {
+  name: string;
+  study_count: number;
+  path: string;
+}
+
+export interface ListTopicsResponse {
+  topics: TopicInfo[];
+  total: number;
+}
+
+export interface InboxStudyDetail {
+  study_name: string;
+  inbox_path: string;
+  spec: import('./atomizer-spec').AtomizerSpec;
+  files: {
+    sim: string[];
+    prt: string[];
+    fem: string[];
+  };
+  context_files: string[];
+}
+
+// ============================================================================
+// Finalize Types
+// ============================================================================
+
+export interface FinalizeRequest {
+  topic: string;
+  run_baseline?: boolean;
+}
+
+export interface FinalizeProgress {
+  step: string;
+  progress: number;
+  message: string;
+  completed: boolean;
+  error?: string;
+}
+
+export interface FinalizeResponse {
+  success: boolean;
+  study_name: string;
+  final_path: string;
+  status: SpecStatus;
+  baseline?: BaselineData;
+  readme_generated: boolean;
+}
+
+// ============================================================================
+// README Generation Types
+// ============================================================================
+
+export interface GenerateReadmeRequest {
+  study_name: string;
+}
+
+export interface GenerateReadmeResponse {
+  success: boolean;
+  content: string;
+  path: string;
+}
+
+// ============================================================================
+// Upload Types
+// ============================================================================
+
+export interface UploadFilesResponse {
+  success: boolean;
+  study_name: string;
+  uploaded_files: Array<{
+    name: string;
+    status: 'uploaded' | 'rejected' | 'skipped';
+    path?: string;
+    size?: number;
+    reason?: string;
+  }>;
+  total_uploaded: number;
+}

atomizer-dashboard/frontend/test-results/.last-run.json

@@ -0,0 +1,4 @@
+{
+  "status": "passed",
+  "failedTests": []
+}

atomizer-dashboard/frontend/tests/e2e/home.spec.ts

@@ -0,0 +1,171 @@
+import { test, expect } from '@playwright/test';
+
+/**
+ * Home Page E2E Tests
+ * 
+ * Tests the study list page at /
+ * Covers: study loading, topic expansion, navigation
+ */
+
+test.describe('Home Page - Study List', () => {
+  
+  test.beforeEach(async ({ page }) => {
+    // Navigate to home page
+    await page.goto('/');
+  });
+
+  test('displays page header', async ({ page }) => {
+    // Check header is visible
+    await expect(page.locator('header')).toBeVisible();
+    
+    // Check for key header elements - Studies heading (exact match to avoid Inbox Studies)
+    await expect(page.getByRole('heading', { name: 'Studies', exact: true })).toBeVisible({ timeout: 10000 });
+  });
+
+  test('shows aggregate statistics cards', async ({ page }) => {
+    // Wait for stats to load
+    await expect(page.getByText('Total Studies')).toBeVisible();
+    await expect(page.getByText('Running')).toBeVisible();
+    await expect(page.getByText('Total Trials')).toBeVisible();
+    await expect(page.getByText('Best Overall')).toBeVisible();
+  });
+
+  test('loads studies table with topic folders', async ({ page }) => {
+    // Wait for studies section (exact match to avoid Inbox Studies)
+    await expect(page.getByRole('heading', { name: 'Studies', exact: true })).toBeVisible();
+    
+    // Wait for loading to complete - either see folders or empty state
+    // Folders have "trials" text in them
+    const folderLocator = page.locator('button:has-text("trials")');
+    const emptyStateLocator = page.getByText('No studies found');
+    
+    // Wait for either studies loaded or empty state (10s timeout)
+    await expect(folderLocator.first().or(emptyStateLocator)).toBeVisible({ timeout: 10000 });
+  });
+
+  test('expands topic folder to show studies', async ({ page }) => {
+    // Wait for folders to load
+    const folderButton = page.locator('button:has-text("trials")').first();
+    
+    // Wait for folder to be visible (studies loaded)
+    await expect(folderButton).toBeVisible({ timeout: 10000 });
+    
+    // Click to expand
+    await folderButton.click();
+    
+    // After expansion, study rows should be visible (they have status badges)
+    // Status badges contain: running, completed, idle, paused, not_started
+    const statusBadges = page.locator('span:has-text("running"), span:has-text("completed"), span:has-text("idle"), span:has-text("paused"), span:has-text("not_started")');
+    await expect(statusBadges.first()).toBeVisible({ timeout: 5000 });
+  });
+
+  test('clicking study shows preview panel', async ({ page }) => {
+    // Wait for and expand first folder
+    const folderButton = page.locator('button:has-text("trials")').first();
+    await expect(folderButton).toBeVisible({ timeout: 10000 });
+    await folderButton.click();
+    
+    // Wait for expanded content and click first study row
+    const studyRow = page.locator('.bg-dark-850\\/50 > div').first();
+    await expect(studyRow).toBeVisible({ timeout: 5000 });
+    await studyRow.click();
+    
+    // Preview panel should show with buttons - use exact match to avoid header nav button
+    await expect(page.getByRole('button', { name: 'Canvas', exact: true })).toBeVisible({ timeout: 5000 });
+    await expect(page.getByRole('button', { name: 'Open' })).toBeVisible();
+  });
+
+  test('Open button navigates to dashboard', async ({ page }) => {
+    // Wait for and expand first folder
+    const folderButton = page.locator('button:has-text("trials")').first();
+    await expect(folderButton).toBeVisible({ timeout: 10000 });
+    await folderButton.click();
+    
+    // Wait for and click study row
+    const studyRow = page.locator('.bg-dark-850\\/50 > div').first();
+    await expect(studyRow).toBeVisible({ timeout: 5000 });
+    await studyRow.click();
+    
+    // Wait for and click Open button
+    const openButton = page.getByRole('button', { name: 'Open' });
+    await expect(openButton).toBeVisible({ timeout: 5000 });
+    await openButton.click();
+    
+    // Should navigate to dashboard
+    await expect(page).toHaveURL(/\/dashboard/);
+  });
+
+  test('Canvas button navigates to canvas view', async ({ page }) => {
+    // Wait for and expand first folder
+    const folderButton = page.locator('button:has-text("trials")').first();
+    await expect(folderButton).toBeVisible({ timeout: 10000 });
+    await folderButton.click();
+    
+    // Wait for and click study row
+    const studyRow = page.locator('.bg-dark-850\\/50 > div').first();
+    await expect(studyRow).toBeVisible({ timeout: 5000 });
+    await studyRow.click();
+    
+    // Wait for and click Canvas button (exact match to avoid header nav)
+    const canvasButton = page.getByRole('button', { name: 'Canvas', exact: true });
+    await expect(canvasButton).toBeVisible({ timeout: 5000 });
+    await canvasButton.click();
+    
+    // Should navigate to canvas
+    await expect(page).toHaveURL(/\/canvas\//);
+  });
+
+  test('refresh button reloads studies', async ({ page }) => {
+    // Find the main studies section refresh button (the one with visible text "Refresh")
+    const refreshButton = page.getByText('Refresh');
+    await expect(refreshButton).toBeVisible({ timeout: 5000 });
+    
+    // Click refresh
+    await refreshButton.click();
+    
+    // Should show loading state or complete quickly
+    // Just verify no errors occurred (exact match to avoid Inbox Studies)
+    await expect(page.getByRole('heading', { name: 'Studies', exact: true })).toBeVisible();
+  });
+});
+
+/**
+ * Inbox Section Tests
+ * 
+ * Tests the new study intake workflow
+ */
+test.describe('Home Page - Inbox Section', () => {
+  
+  test.beforeEach(async ({ page }) => {
+    await page.goto('/');
+  });
+
+  test('displays inbox section with header', async ({ page }) => {
+    // Check for Study Inbox heading (section is expanded by default)
+    const inboxHeading = page.getByRole('heading', { name: 'Study Inbox' });
+    await expect(inboxHeading).toBeVisible({ timeout: 10000 });
+  });
+
+  test('inbox section shows pending count', async ({ page }) => {
+    // Section should show pending studies count
+    const pendingText = page.getByText(/\d+ pending studies/);
+    await expect(pendingText).toBeVisible({ timeout: 10000 });
+  });
+
+  test('inbox has new study button', async ({ page }) => {
+    // Section is expanded by default, look for the New Study button
+    const newStudyButton = page.getByRole('button', { name: /New Study/ });
+    await expect(newStudyButton).toBeVisible({ timeout: 10000 });
+  });
+
+  test('clicking new study shows create form', async ({ page }) => {
+    // Click the New Study button
+    const newStudyButton = page.getByRole('button', { name: /New Study/ });
+    await expect(newStudyButton).toBeVisible({ timeout: 10000 });
+    await newStudyButton.click();
+    
+    // Form should expand with input fields
+    const studyNameInput = page.getByPlaceholder(/my_study/i).or(page.locator('input[type="text"]').first());
+    await expect(studyNameInput).toBeVisible({ timeout: 5000 });
+  });
+});

atomizer-field/.gitignore

@@ -1,56 +0,0 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-env/
-venv/
-ENV/
-*.egg-info/
-dist/
-build/
-
-# Jupyter Notebooks
-.ipynb_checkpoints/
-*.ipynb
-
-# IDEs
-.vscode/
-.idea/
-*.swp
-*.swo
-*~
-
-# OS
-.DS_Store
-Thumbs.db
-
-# Data files (large)
-*.op2
-*.bdf
-*.dat
-*.f06
-*.pch
-*.h5
-*.hdf5
-
-# Training data
-training_data/
-checkpoints/
-runs/
-logs/
-
-# Test outputs
-test_case_*/
-visualization_images/
-
-# Temporary files
-*.tmp
-*.log
-*.bak
-*.orig
-
-# Environment
-atomizer_env/
-.conda/

atomizer-field/ATOMIZER_FIELD_STATUS_REPORT.md

@@ -1,635 +0,0 @@
-# AtomizerField - Complete Status Report
-
-**Date:** November 24, 2025
-**Version:** 1.0
-**Status:** ✅ Core System Operational, Unit Issues Resolved
-
----
-
-## Executive Summary
-
-**AtomizerField** is a neural field learning system that replaces traditional FEA simulations with graph neural networks, providing **1000× faster predictions** for structural optimization.
-
-### Current Status
-- ✅ **Core pipeline working**: BDF/OP2 → Neural format → GNN inference
-- ✅ **Test case validated**: Simple Beam (5,179 nodes, 4,866 elements)
-- ✅ **Unit system understood**: MN-MM system (kPa stress, N forces, mm length)
-- ⚠️ **Not yet trained**: Neural network has random weights
-- 🔜 **Next step**: Generate training data and train model
-
----
-
-## What AtomizerField Does
-
-### 1. Data Pipeline ✅ WORKING
-
-**Purpose:** Convert Nastran FEA results into neural network training data
-
-**Input:**
-- BDF file (geometry, materials, loads, BCs)
-- OP2 file (FEA results: displacement, stress, reactions)
-
-**Output:**
-- JSON metadata (mesh, materials, loads, statistics)
-- HDF5 arrays (coordinates, displacement, stress fields)
-
-**What's Extracted:**
-- ✅ Mesh: 5,179 nodes, 4,866 CQUAD4 shell elements
-- ✅ Materials: Young's modulus, Poisson's ratio, density
-- ✅ Boundary conditions: SPCs, MPCs (if present)
-- ✅ Loads: 35 point forces with directions
-- ✅ Displacement field: 6 DOF per node (Tx, Ty, Tz, Rx, Ry, Rz)
-- ✅ Stress field: 8 components per element (σxx, σyy, τxy, principals, von Mises)
-- ✅ Reaction forces: 6 DOF per node
-
-**Performance:**
-- Parse time: 1.27 seconds
-- Data size: JSON 1.7 MB, HDF5 546 KB
-
-### 2. Graph Neural Network ✅ ARCHITECTURE WORKING
-
-**Purpose:** Learn FEA physics to predict displacement/stress from geometry/loads
-
-**Architecture:**
-- Type: Graph Neural Network (PyTorch Geometric)
-- Parameters: 128,589 (small model for testing)
-- Layers: 6 message passing layers
-- Hidden dimension: 64
-
-**Input Features:**
-- Node features (12D): position (3D), BCs (6 DOF), loads (3D)
-- Edge features (5D): E, ν, ρ, G, α (material properties)
-
-**Output Predictions:**
-- Displacement: (N_nodes, 6) - full 6 DOF per node
-- Stress: (N_elements, 6) - stress tensor components
-- Von Mises: (N_elements,) - scalar stress measure
-
-**Current State:**
-- ✅ Model instantiates successfully
-- ✅ Forward pass works
-- ✅ Inference time: 95.94 ms (< 100 ms target)
-- ⚠️ Predictions are random (untrained weights)
-
-### 3. Visualization ✅ WORKING
-
-**Purpose:** Visualize mesh, displacement, and stress fields
-
-**Capabilities:**
-- ✅ 3D mesh rendering (nodes + elements)
-- ✅ Displacement visualization (original + deformed)
-- ✅ Stress field coloring (von Mises)
-- ✅ Automatic report generation (markdown + images)
-
-**Generated Outputs:**
-- mesh.png (227 KB)
-- displacement.png (335 KB)
-- stress.png (215 KB)
-- Markdown report with embedded images
-
-### 4. Unit System ✅ UNDERSTOOD
-
-**Nastran UNITSYS: MN-MM**
-
-Despite the name, actual units are:
-- Length: **mm** (millimeter)
-- Force: **N** (Newton) - NOT MegaNewton!
-- Stress: **kPa** (kiloPascal = N/mm²) - NOT MPa!
-- Mass: **kg** (kilogram)
-- Young's modulus: **kPa** (200,000,000 kPa = 200 GPa for steel)
-
-**Validated Values:**
-- Max stress: 117,000 kPa = **117 MPa** ✓ (reasonable for steel)
-- Max displacement: **19.5 mm** ✓
-- Applied forces: **~2.73 MN each** ✓ (large beam structure)
-- Young's modulus: 200,000,000 kPa = **200 GPa** ✓ (steel)
-
-### 5. Direction Handling ✅ FULLY VECTORIAL
-
-**All fields preserve directional information:**
-
-**Displacement (6 DOF):**
-```
-[Tx, Ty, Tz, Rx, Ry, Rz]
-```
-- Stored as (5179, 6) array
-- Full translation + rotation at each node
-
-**Forces/Reactions (6 DOF):**
-```
-[Fx, Fy, Fz, Mx, My, Mz]
-```
-- Stored as (5179, 6) array
-- Full force + moment vectors
-
-**Stress Tensor (shell elements):**
-```
-[fiber_distance, σxx, σyy, τxy, angle, σ_major, σ_minor, von_mises]
-```
-- Stored as (9732, 8) array
-- Full stress state for each element (2 per CQUAD4)
-
-**Coordinate System:**
-- Global XYZ coordinates
-- Node positions: (5179, 3) array
-- Element connectivity preserves topology
-
-**Neural Network:**
-- Learns directional relationships through graph structure
-- Message passing propagates forces through mesh topology
-- Predicts full displacement vectors and stress tensors
-
----
-
-## What's Been Tested
-
-### ✅ Smoke Tests (5/5 PASS)
-
-1. **Model Creation**: GNN instantiates with 128,589 parameters
-2. **Forward Pass**: Processes dummy graph data
-3. **Loss Functions**: All 4 loss types compute correctly
-4. **Batch Processing**: Handles batched data
-5. **Gradient Flow**: Backpropagation works
-
-**Status:** All passing, system fundamentally sound
-
-### ✅ Simple Beam End-to-End Test (7/7 PASS)
-
-1. **File Existence**: BDF (1,230 KB) and OP2 (4,461 KB) found
-2. **Directory Setup**: test_case_beam/ structure created
-3. **Module Imports**: All dependencies load correctly
-4. **BDF/OP2 Parsing**: 5,179 nodes, 4,866 elements extracted
-5. **Data Validation**: No NaN values, physics consistent
-6. **Graph Conversion**: PyTorch Geometric format successful
-7. **Neural Prediction**: Inference in 95.94 ms
-
-**Status:** Complete pipeline validated with real FEA data
-
-### ✅ Visualization Test
-
-1. **Mesh Rendering**: 5,179 nodes, 4,866 elements displayed
-2. **Displacement Field**: Original + deformed (10× scale)
-3. **Stress Field**: Von Mises coloring across elements
-4. **Report Generation**: Markdown + embedded images
-
-**Status:** All visualizations working correctly
-
-### ✅ Unit Validation
-
-1. **UNITSYS Detection**: MN-MM system identified
-2. **Material Properties**: E = 200 GPa confirmed for steel
-3. **Stress Values**: 117 MPa reasonable for loaded beam
-4. **Force Values**: 2.73 MN per load point validated
-
-**Status:** Units understood, values physically realistic
-
----
-
-## What's NOT Tested Yet
-
-### ❌ Physics Validation Tests (0/4)
-
-These require **trained model**:
-
-1. **Cantilever Beam Test**: Analytical solution comparison
-   - Load known geometry/loads
-   - Compare prediction vs analytical deflection formula
-   - Target: < 5% error
-
-2. **Equilibrium Test**: ∇·σ + f = 0
-   - Check force balance at each node
-   - Ensure physics laws satisfied
-   - Target: Residual < 1% of max force
-
-3. **Constitutive Law Test**: σ = C:ε (Hooke's law)
-   - Verify stress-strain relationship
-   - Check material model accuracy
-   - Target: < 5% deviation
-
-4. **Energy Conservation Test**: Strain energy = work done
-   - Compute ∫(σ:ε)dV vs ∫(f·u)dV
-   - Ensure energy balance
-   - Target: < 5% difference
-
-**Blocker:** Model not trained yet (random weights)
-
-### ❌ Learning Tests (0/4)
-
-These require **trained model**:
-
-1. **Memorization Test**: Can model fit single example?
-   - Train on 1 case, test on same case
-   - Target: < 1% error (proves capacity)
-
-2. **Interpolation Test**: Can model predict between training cases?
-   - Train on cases A and C
-   - Test on case B (intermediate)
-   - Target: < 10% error
-
-3. **Extrapolation Test**: Can model generalize?
-   - Train on small loads
-   - Test on larger loads
-   - Target: < 20% error (harder)
-
-4. **Pattern Recognition Test**: Does model learn physics?
-   - Test on different geometry with same physics
-   - Check if physical principles transfer
-   - Target: Qualitative correctness
-
-**Blocker:** Model not trained yet
-
-### ❌ Integration Tests (0/5)
-
-These require **trained model + optimization interface**:
-
-1. **Batch Prediction**: Process multiple designs
-2. **Gradient Computation**: Analytical sensitivities
-3. **Optimization Loop**: Full design cycle
-4. **Uncertainty Quantification**: Ensemble predictions
-5. **Online Learning**: Update during optimization
-
-**Blocker:** Model not trained yet
-
-### ❌ Performance Tests (0/3)
-
-These require **trained model**:
-
-1. **Accuracy Benchmark**: < 10% error vs FEA
-2. **Speed Benchmark**: < 50 ms inference time
-3. **Scalability Test**: Larger meshes (10K+ nodes)
-
-**Blocker:** Model not trained yet
-
----
-
-## Current Capabilities Summary
-
-| Feature | Status | Notes |
-|---------|--------|-------|
-| **Data Pipeline** | ✅ Working | Parses BDF/OP2 to neural format |
-| **Unit Handling** | ✅ Understood | MN-MM system (kPa stress, N force) |
-| **Direction Handling** | ✅ Complete | Full 6 DOF + tensor components |
-| **Graph Conversion** | ✅ Working | PyTorch Geometric format |
-| **GNN Architecture** | ✅ Working | 128K params, 6 layers |
-| **Forward Pass** | ✅ Working | 95.94 ms inference |
-| **Visualization** | ✅ Working | 3D mesh, displacement, stress |
-| **Training Pipeline** | ⚠️ Ready | Code exists, not executed |
-| **Physics Compliance** | ❌ Unknown | Requires trained model |
-| **Prediction Accuracy** | ❌ Unknown | Requires trained model |
-
----
-
-## Known Issues
-
-### ⚠️ Minor Issues
-
-1. **Unit Labels**: Parser labels stress as "MPa" when it's actually "kPa"
-   - Impact: Confusing but documented
-   - Fix: Update labels in neural_field_parser.py
-   - Priority: Low (doesn't affect calculations)
-
-2. **Unicode Encoding**: Windows cp1252 codec limitations
-   - Impact: Crashes with Unicode symbols (✓, →, σ, etc.)
-   - Fix: Already replaced most with ASCII
-   - Priority: Low (cosmetic)
-
-3. **No SPCs Found**: Test beam has no explicit constraints
-   - Impact: Warning message appears
-   - Fix: Probably fixed at edges (investigate BDF)
-   - Priority: Low (analysis ran successfully)
-
-### ✅ Resolved Issues
-
-1. ~~**NumPy MINGW-W64 Crashes**~~
-   - Fixed: Created conda environment with proper NumPy
-   - Status: All tests running without crashes
-
-2. ~~**pyNastran API Compatibility**~~
-   - Fixed: Added getattr/hasattr checks for optional attributes
-   - Status: Parser handles missing 'sol' and 'temps'
-
-3. ~~**Element Connectivity Structure**~~
-   - Fixed: Discovered categorized dict structure (solid/shell/beam)
-   - Status: Visualization working correctly
-
-4. ~~**Node ID Mapping**~~
-   - Fixed: Created node_id_to_idx mapping for 1-indexed IDs
-   - Status: Element plotting correct
-
----
-
-## What's Next
-
-### Phase 1: Fix Unit Labels (30 minutes)
-
-**Goal:** Update parser to correctly label units
-
-**Changes needed:**
-```python
-# neural_field_parser.py line ~623
-"units": "kPa"  # Changed from "MPa"
-
-# metadata section
-"stress": "kPa"  # Changed from "MPa"
-```
-
-**Validation:**
-- Re-run test_simple_beam.py
-- Check reports show "117 kPa" not "117 MPa"
-- Or add conversion: stress/1000 → MPa
-
-### Phase 2: Generate Training Data (1-2 weeks)
-
-**Goal:** Create 50-500 training cases
-
-**Approach:**
-1. Vary beam dimensions (length, width, thickness)
-2. Vary loading conditions (magnitude, direction, location)
-3. Vary material properties (steel, aluminum, titanium)
-4. Vary boundary conditions (cantilever, simply supported, clamped)
-
-**Expected:**
-- 50 minimum (quick validation)
-- 200 recommended (good accuracy)
-- 500 maximum (best performance)
-
-**Tools:**
-- Use parametric FEA (NX Nastran)
-- Batch processing script
-- Quality validation for each case
-
-### Phase 3: Train Neural Network (2-6 hours)
-
-**Goal:** Train model to < 10% prediction error
-
-**Configuration:**
-```bash
-python train.py \
-  --data_dirs training_data/* \
-  --epochs 100 \
-  --batch_size 16 \
-  --lr 0.001 \
-  --loss physics \
-  --checkpoint_dir checkpoints/
-```
-
-**Expected:**
-- Training time: 2-6 hours (CPU)
-- Loss convergence: < 0.01
-- Validation error: < 10%
-
-**Monitoring:**
-- TensorBoard for loss curves
-- Validation metrics every 10 epochs
-- Early stopping if no improvement
-
-### Phase 4: Validate Performance (1-2 hours)
-
-**Goal:** Run full test suite
-
-**Tests:**
-```bash
-# Physics tests
-python test_suite.py --physics
-
-# Learning tests
-python test_suite.py --learning
-
-# Full validation
-python test_suite.py --full
-```
-
-**Expected:**
-- All 18 tests passing
-- Physics compliance < 5% error
-- Prediction accuracy < 10% error
-- Inference time < 50 ms
-
-### Phase 5: Production Deployment (1 day)
-
-**Goal:** Integrate with Atomizer
-
-**Interface:**
-```python
-from optimization_interface import NeuralFieldOptimizer
-
-optimizer = NeuralFieldOptimizer('checkpoints/best_model.pt')
-results = optimizer.evaluate(design_graph)
-sensitivities = optimizer.get_sensitivities(design_graph)
-```
-
-**Features:**
-- Fast evaluation: ~10 ms per design
-- Analytical gradients: 1M× faster than finite differences
-- Uncertainty quantification: Confidence intervals
-- Online learning: Improve during optimization
-
----
-
-## Testing Strategy
-
-### Current: Smoke Testing ✅
-
-**Status:** Completed
-- 5/5 smoke tests passing
-- 7/7 end-to-end tests passing
-- System fundamentally operational
-
-### Next: Unit Testing
-
-**What to test:**
-- Individual parser functions
-- Data validation rules
-- Unit conversion functions
-- Graph construction logic
-
-**Priority:** Medium (system working, but good for maintainability)
-
-### Future: Integration Testing
-
-**What to test:**
-- Multi-case batch processing
-- Training pipeline end-to-end
-- Optimization interface
-- Uncertainty quantification
-
-**Priority:** High (required before production)
-
-### Future: Physics Testing
-
-**What to test:**
-- Analytical solution comparison
-- Energy conservation
-- Force equilibrium
-- Constitutive laws
-
-**Priority:** Critical (validates correctness)
-
----
-
-## Performance Expectations
-
-### After Training
-
-| Metric | Target | Expected |
-|--------|--------|----------|
-| Prediction Error | < 10% | 5-10% |
-| Inference Time | < 50 ms | 10-30 ms |
-| Speedup vs FEA | 1000× | 1000-3000× |
-| Memory Usage | < 500 MB | ~300 MB |
-
-### Production Capability
-
-**Single Evaluation:**
-- FEA: 30-300 seconds
-- Neural: 10-30 ms
-- **Speedup: 1000-10,000×**
-
-**Optimization Loop (100 iterations):**
-- FEA: 50-500 minutes
-- Neural: 1-3 seconds
-- **Speedup: 3000-30,000×**
-
-**Gradient Computation:**
-- FEA (finite diff): 300-3000 seconds
-- Neural (analytical): 0.1 ms
-- **Speedup: 3,000,000-30,000,000×**
-
----
-
-## Risk Assessment
-
-### Low Risk ✅
-
-- Core pipeline working
-- Data extraction validated
-- Units understood
-- Visualization working
-
-### Medium Risk ⚠️
-
-- Model architecture untested with training
-- Physics compliance unknown
-- Generalization capability unclear
-- Need diverse training data
-
-### High Risk ❌
-
-- None identified currently
-
-### Mitigation Strategies
-
-1. **Start with small dataset** (50 cases) to validate training
-2. **Monitor physics losses** during training
-3. **Test on analytical cases** first (cantilever beam)
-4. **Gradual scaling** to larger/more complex geometries
-
----
-
-## Resource Requirements
-
-### Computational
-
-**Training:**
-- CPU: 8+ cores recommended
-- RAM: 16 GB minimum
-- GPU: Optional (10× faster, 8+ GB VRAM)
-- Time: 2-6 hours
-
-**Inference:**
-- CPU: Any (even single core works)
-- RAM: 2 GB sufficient
-- GPU: Not needed
-- Time: 10-30 ms per case
-
-### Data Storage
-
-**Per Training Case:**
-- BDF: ~1 MB
-- OP2: ~5 MB
-- Parsed (JSON): ~2 MB
-- Parsed (HDF5): ~500 KB
-- **Total: ~8.5 MB per case**
-
-**Full Training Set (200 cases):**
-- Raw: ~1.2 GB
-- Parsed: ~500 MB
-- Model: ~2 MB
-- **Total: ~1.7 GB**
-
----
-
-## Recommendations
-
-### Immediate (This Week)
-
-1. ✅ **Fix unit labels** - 30 minutes
-   - Update "MPa" → "kPa" in parser
-   - Or add /1000 conversion to match expected units
-
-2. **Document unit system** - 1 hour
-   - Add comments in parser
-   - Update user documentation
-   - Create unit conversion guide
-
-### Short-term (Next 2 Weeks)
-
-3. **Generate training data** - 1-2 weeks
-   - Start with 50 cases (minimum viable)
-   - Validate data quality
-   - Expand to 200 if needed
-
-4. **Initial training** - 1 day
-   - Train on 50 cases
-   - Validate on 10 held-out cases
-   - Check physics compliance
-
-### Medium-term (Next Month)
-
-5. **Full validation** - 1 week
-   - Run complete test suite
-   - Physics compliance tests
-   - Accuracy benchmarks
-
-6. **Production integration** - 1 week
-   - Connect to Atomizer
-   - End-to-end optimization test
-   - Performance profiling
-
----
-
-## Conclusion
-
-### ✅ What's Working
-
-AtomizerField has a **fully functional core pipeline**:
-- Parses real FEA data (5,179 nodes validated)
-- Converts to neural network format
-- GNN architecture operational (128K params)
-- Inference runs fast (95.94 ms)
-- Visualization produces publication-quality figures
-- Units understood and validated
-
-### 🔜 What's Next
-
-The system is **ready for training**:
-- All infrastructure in place
-- Test case validated
-- Neural architecture proven
-- Just needs training data
-
-### 🎯 Production Readiness
-
-**After training (2-3 weeks):**
-- Prediction accuracy: < 10% error
-- Inference speed: 1000× faster than FEA
-- Full integration with Atomizer
-- **Revolutionary optimization capability unlocked!**
-
-The hard work is done - now we train and deploy! 🚀
-
----
-
-*Report generated: November 24, 2025*
-*AtomizerField v1.0*
-*Status: Core operational, ready for training*

atomizer-field/AtomizerField_Development_Report.md

@@ -1,603 +0,0 @@
-# AtomizerField Development Report
-
-**Prepared for:** Antoine Polvé  
-**Date:** November 24, 2025  
-**Status:** Core System Complete → Ready for Training Phase
-
----
-
-## Executive Summary
-
-AtomizerField is **fully implemented and validated** at the architectural level. The project has achieved approximately **~7,000 lines of production code** across all phases, with a complete data pipeline, neural network architecture, physics-informed training system, and optimization interface. 
-
-**Current Position:** You're at the transition point between "building" and "training/deploying."
-
-**Critical Insight:** The system works—now it needs data to learn from.
-
----
-
-## Part 1: Current Development Status
-
-### What's Built ✅
-
-| Component | Status | Lines of Code | Validation |
-|-----------|--------|---------------|------------|
-| **BDF/OP2 Parser** | ✅ Complete | ~1,400 | Tested with Simple Beam |
-| **Graph Neural Network** | ✅ Complete | ~490 | 718,221 parameters, forward pass validated |
-| **Physics-Informed Losses** | ✅ Complete | ~450 | All 4 loss types tested |
-| **Data Loader** | ✅ Complete | ~420 | PyTorch Geometric integration |
-| **Training Pipeline** | ✅ Complete | ~430 | TensorBoard, checkpointing, early stopping |
-| **Inference Engine** | ✅ Complete | ~380 | 95ms inference time validated |
-| **Optimization Interface** | ✅ Complete | ~430 | Drop-in FEA replacement ready |
-| **Uncertainty Quantification** | ✅ Complete | ~380 | Ensemble-based, online learning |
-| **Test Suite** | ✅ Complete | ~2,700 | 18 automated tests |
-| **Documentation** | ✅ Complete | 10 guides | Comprehensive coverage |
-
-### Simple Beam Validation Results
-
-Your actual FEA model was successfully processed:
-
-```
-✅ Nodes parsed:        5,179
-✅ Elements parsed:     4,866 CQUAD4
-✅ Displacement field:  Complete (max: 19.56 mm)
-✅ Stress field:        Complete (9,732 values)
-✅ Graph conversion:    PyTorch Geometric format
-✅ Neural inference:    95.94 ms
-✅ All 7 tests:         PASSED
-```
-
-### What's NOT Done Yet ⏳
-
-| Gap | Impact | Effort Required |
-|-----|--------|-----------------|
-| **Training data generation** | Can't train without data | 1-2 weeks (50-500 cases) |
-| **Model training** | Model has random weights | 2-8 hours (GPU) |
-| **Physics validation** | Can't verify accuracy | After training |
-| **Atomizer integration** | Not connected yet | 1-2 weeks |
-| **Production deployment** | Not in optimization loop | After integration |
-
----
-
-## Part 2: The Physics-Neural Network Architecture
-
-### Core Innovation: Learning Fields, Not Scalars
-
-**Traditional Approach:**
-```
-Design Parameters → FEA (30 min) → max_stress = 450 MPa (1 number)
-```
-
-**AtomizerField Approach:**
-```
-Design Parameters → Neural Network (50 ms) → stress_field[5,179 nodes × 6 components]
-                                           = 31,074 stress values!
-```
-
-This isn't just faster—it's fundamentally different. You know **WHERE** the stress is, not just **HOW MUCH**.
-
-### The Graph Neural Network Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                    GRAPH REPRESENTATION                          │
-├─────────────────────────────────────────────────────────────────┤
-│  NODES (from FEA mesh):                                         │
-│  ├── Position (x, y, z)           → 3 features                  │
-│  ├── Boundary conditions (6 DOF)  → 6 features (0/1 mask)       │
-│  └── Applied loads (Fx, Fy, Fz)   → 3 features                  │
-│      Total: 12 features per node                                │
-│                                                                  │
-│  EDGES (from element connectivity):                              │
-│  ├── Young's modulus (E)          → Material stiffness          │
-│  ├── Poisson's ratio (ν)          → Lateral contraction         │
-│  ├── Density (ρ)                  → Mass distribution           │
-│  ├── Shear modulus (G)            → Shear behavior              │
-│  └── Thermal expansion (α)        → Thermal effects             │
-│      Total: 5 features per edge                                 │
-└─────────────────────────────────────────────────────────────────┘
-                              ↓
-┌─────────────────────────────────────────────────────────────────┐
-│                    MESSAGE PASSING (6 LAYERS)                    │
-├─────────────────────────────────────────────────────────────────┤
-│  Each layer:                                                     │
-│  1. Gather neighbor information                                  │
-│  2. Weight by material properties (edge features)                │
-│  3. Update node representation                                   │
-│  4. Residual connection + LayerNorm                             │
-│                                                                  │
-│  KEY INSIGHT: Forces propagate through connected elements!       │
-│  The network learns HOW forces flow through the structure.       │
-└─────────────────────────────────────────────────────────────────┘
-                              ↓
-┌─────────────────────────────────────────────────────────────────┐
-│                    FIELD PREDICTIONS                             │
-├─────────────────────────────────────────────────────────────────┤
-│  Displacement: [N_nodes, 6]  → Tx, Ty, Tz, Rx, Ry, Rz           │
-│  Stress:       [N_nodes, 6]  → σxx, σyy, σzz, τxy, τyz, τxz     │
-│  Von Mises:    [N_nodes, 1]  → Scalar stress measure            │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-### Physics-Informed Loss Functions
-
-The network doesn't just minimize prediction error—it enforces physical laws:
-
-```
-L_total = λ_data × L_data           # Match FEA results
-        + λ_eq × L_equilibrium       # ∇·σ + f = 0 (force balance)
-        + λ_const × L_constitutive   # σ = C:ε (Hooke's law)
-        + λ_bc × L_boundary          # u = 0 at fixed nodes
-```
-
-**Why This Matters:**
-- **Faster convergence:** Network starts with physics intuition
-- **Better generalization:** Extrapolates correctly outside training range
-- **Physically plausible:** No "impossible" stress distributions
-- **Less data needed:** Physics provides strong inductive bias
-
-### What Makes This Different from Standard PINNs
-
-| Aspect | Academic PINNs | AtomizerField |
-|--------|----------------|---------------|
-| **Geometry** | Simple (rods, plates) | Complex industrial meshes |
-| **Data source** | Solve PDEs from scratch | Learn from existing FEA |
-| **Goal** | Replace physics solvers | Accelerate optimization |
-| **Mesh** | Regular grids | Arbitrary unstructured |
-| **Scalability** | ~100s of DOFs | ~50,000+ DOFs |
-
-AtomizerField is better described as a **"Data-Driven Surrogate Model for Structural Optimization"** or **"FEA-Informed Neural Network."**
-
----
-
-## Part 3: How to Test a Concrete Solution
-
-### Step 1: Generate Training Data (Critical Path)
-
-You need **50-500 FEA cases** with geometric/load variations.
-
-**Option A: Parametric Study in NX (Recommended)**
-
-```
-For your Simple Beam:
-1. Open beam_sim1 in NX
-2. Create design study with variations:
-   - Thickness: 1mm, 2mm, 3mm, 4mm, 5mm
-   - Width: 50mm, 75mm, 100mm
-   - Load: 1000N, 2000N, 3000N, 4000N
-   - Support position: 3 locations
-   
-   Total: 5 × 3 × 4 × 3 = 180 cases
-   
-3. Run all cases (automated with NX journal)
-4. Export BDF/OP2 for each case
-```
-
-**Option B: Design of Experiments**
-
-```python
-# Generate Latin Hypercube sampling
-import numpy as np
-from scipy.stats import qmc
-
-sampler = qmc.LatinHypercube(d=4)  # 4 design variables
-sample = sampler.random(n=100)     # 100 cases
-
-# Scale to your design space
-thickness = 1 + sample[:, 0] * 4    # 1-5 mm
-width = 50 + sample[:, 1] * 50      # 50-100 mm
-load = 1000 + sample[:, 2] * 3000   # 1000-4000 N
-# etc.
-```
-
-**Option C: Monte Carlo Sampling**
-
-Generate random combinations within bounds. Quick but less space-filling than LHS.
-
-### Step 2: Parse All Training Data
-
-```bash
-# Create directory structure
-mkdir training_data
-mkdir validation_data
-
-# Move 80% of cases to training, 20% to validation
-
-# Batch parse
-python batch_parser.py --input training_data/ --output parsed_training/
-python batch_parser.py --input validation_data/ --output parsed_validation/
-```
-
-### Step 3: Train the Model
-
-```bash
-# Initial training (MSE only)
-python train.py \
-    --data_dirs parsed_training/* \
-    --epochs 50 \
-    --batch_size 16 \
-    --loss mse \
-    --checkpoint_dir checkpoints/mse/
-
-# Physics-informed training (recommended)
-python train.py \
-    --data_dirs parsed_training/* \
-    --epochs 100 \
-    --batch_size 16 \
-    --loss physics \
-    --checkpoint_dir checkpoints/physics/
-
-# Monitor progress
-tensorboard --logdir runs/
-```
-
-**Expected Training Time:**
-- CPU: 6-24 hours (50-500 cases)
-- GPU: 1-4 hours (much faster)
-
-### Step 4: Validate the Trained Model
-
-```bash
-# Run full test suite
-python test_suite.py --full
-
-# Test on validation set
-python predict.py \
-    --model checkpoints/physics/best_model.pt \
-    --data parsed_validation/ \
-    --compare
-
-# Expected metrics:
-# - Displacement error: < 10%
-# - Stress error: < 15%
-# - Inference time: < 50ms
-```
-
-### Step 5: Quick Smoke Test (Do This First!)
-
-Before generating 500 cases, test with 10 cases:
-
-```bash
-# Generate 10 quick variations
-# Parse them
-python batch_parser.py --input quick_test/ --output parsed_quick/
-
-# Train for 20 epochs (5 minutes)
-python train.py \
-    --data_dirs parsed_quick/* \
-    --epochs 20 \
-    --batch_size 4
-
-# Check if loss decreases → Network is learning!
-```
-
----
-
-## Part 4: What Should Be Implemented Next
-
-### Immediate Priorities (This Week)
-
-| Task | Purpose | Effort |
-|------|---------|--------|
-| **1. Generate 10 test cases** | Validate learning capability | 2-4 hours |
-| **2. Run quick training** | Prove network learns | 30 min |
-| **3. Visualize predictions** | See if fields make sense | 1 hour |
-
-### Short-Term (Next 2 Weeks)
-
-| Task | Purpose | Effort |
-|------|---------|--------|
-| **4. Generate 100+ training cases** | Production-quality data | 1 week |
-| **5. Full training run** | Trained model | 4-8 hours |
-| **6. Physics validation** | Cantilever beam test | 2 hours |
-| **7. Accuracy benchmarks** | Quantify error rates | 4 hours |
-
-### Medium-Term (1-2 Months)
-
-| Task | Purpose | Effort |
-|------|---------|--------|
-| **8. Atomizer integration** | Connect to optimization loop | 1-2 weeks |
-| **9. Uncertainty deployment** | Know when to trust | 1 week |
-| **10. Online learning** | Improve during optimization | 1 week |
-| **11. Multi-project transfer** | Reuse across designs | 2 weeks |
-
-### Code That Needs Writing
-
-**1. Automated Training Data Generator** (~200 lines)
-```python
-# generate_training_data.py
-class TrainingDataGenerator:
-    """Generate parametric FEA studies for training"""
-    
-    def generate_parametric_study(self, base_model, variations):
-        # Create NX journal for parametric study
-        # Run all cases automatically
-        # Collect BDF/OP2 pairs
-        pass
-```
-
-**2. Transfer Learning Module** (~150 lines)
-```python
-# transfer_learning.py
-class TransferLearningManager:
-    """Adapt trained model to new project"""
-    
-    def fine_tune(self, base_model, new_data, freeze_layers=4):
-        # Freeze early layers (general physics)
-        # Train later layers (project-specific)
-        pass
-```
-
-**3. Real-Time Visualization** (~300 lines)
-```python
-# field_visualizer.py
-class RealTimeFieldVisualizer:
-    """Interactive 3D visualization of predicted fields"""
-    
-    def show_prediction(self, design, prediction):
-        # 3D mesh with displacement
-        # Color by stress
-        # Slider for design parameters
-        pass
-```
-
----
-
-## Part 5: Atomizer Integration Strategy
-
-### Current Atomizer Architecture
-
-```
-Atomizer (Main Platform)
-├── optimization_engine/
-│   ├── runner.py           # Manages optimization loop
-│   ├── multi_optimizer.py  # Optuna optimization
-│   └── hook_manager.py     # Plugin system
-├── nx_journals/
-│   └── update_and_solve.py # NX FEA automation
-└── dashboard/
-    └── React frontend      # Real-time monitoring
-```
-
-### Integration Points
-
-**1. Replace FEA Calls (Primary Integration)**
-
-In `runner.py`, replace:
-```python
-# Before
-def evaluate_design(self, parameters):
-    self.nx_solver.update_parameters(parameters)
-    self.nx_solver.run_fea()  # 30 minutes
-    results = self.nx_solver.extract_results()
-    return results
-```
-
-With:
-```python
-# After
-from atomizer_field import NeuralFieldOptimizer
-
-def evaluate_design(self, parameters):
-    # First: Neural prediction (50ms)
-    graph = self.build_graph(parameters)
-    prediction = self.neural_optimizer.predict(graph)
-    
-    # Check uncertainty
-    if prediction['uncertainty'] > 0.1:
-        # High uncertainty: run FEA for validation
-        self.nx_solver.run_fea()
-        fea_results = self.nx_solver.extract_results()
-        
-        # Update model online
-        self.neural_optimizer.update(graph, fea_results)
-        return fea_results
-    
-    return prediction  # Trust neural network
-```
-
-**2. Gradient-Based Optimization**
-
-Current Atomizer uses Optuna (TPE, GP). With AtomizerField:
-
-```python
-# Add gradient-based option
-from atomizer_field import NeuralFieldOptimizer
-
-optimizer = NeuralFieldOptimizer('model.pt')
-
-# Analytical gradients (instant!)
-gradients = optimizer.get_sensitivities(design_graph)
-
-# Gradient descent optimization
-for iteration in range(100):
-    gradients = optimizer.get_sensitivities(current_design)
-    current_design -= learning_rate * gradients  # Direct update!
-```
-
-**Benefits:**
-- 1,000,000× faster than finite differences
-- Can optimize 100+ parameters efficiently
-- Better local convergence
-
-**3. Dashboard Integration**
-
-Add neural prediction tab to React dashboard:
-- Real-time field visualization
-- Prediction vs FEA comparison
-- Uncertainty heatmap
-- Training progress monitoring
-
-### Integration Roadmap
-
-```
-Week 1-2: Basic Integration
-├── Add AtomizerField as dependency
-├── Create neural_evaluator.py in optimization_engine/
-├── Add --use-neural flag to runner
-└── Test on simple_beam_optimization study
-
-Week 3-4: Smart Switching
-├── Implement uncertainty-based FEA triggering
-├── Add online learning updates
-├── Compare optimization quality vs pure FEA
-└── Benchmark speedup
-
-Week 5-6: Full Production
-├── Dashboard integration
-├── Multi-project support
-├── Documentation and examples
-└── Performance profiling
-```
-
-### Expected Benefits After Integration
-
-| Metric | Current (FEA Only) | With AtomizerField |
-|--------|-------------------|-------------------|
-| **Time per evaluation** | 30-300 seconds | 5-50 ms |
-| **Evaluations per hour** | 12-120 | 72,000-720,000 |
-| **Optimization time (1000 trials)** | 8-80 hours | 5-50 seconds + validation FEA |
-| **Gradient computation** | Finite diff (slow) | Analytical (instant) |
-| **Field insights** | Only max values | Complete distributions |
-
-**Conservative Estimate:** 100-1000× speedup with hybrid approach (neural + selective FEA validation)
-
----
-
-## Part 6: Development Gap Analysis
-
-### Code Gaps
-
-| Component | Current State | What's Needed | Effort |
-|-----------|--------------|---------------|--------|
-| Training data generation | Manual | Automated NX journal | 1 week |
-| Real-time visualization | Basic | Interactive 3D | 1 week |
-| Atomizer bridge | Not started | Integration module | 1-2 weeks |
-| Transfer learning | Designed | Implementation | 3-5 days |
-| Multi-solution support | Not started | Extend parser | 3-5 days |
-
-### Testing Gaps
-
-| Test Type | Current | Needed |
-|-----------|---------|--------|
-| Smoke tests | ✅ Complete | - |
-| Physics validation | ⏳ Ready | Run after training |
-| Accuracy benchmarks | ⏳ Ready | Run after training |
-| Integration tests | Not started | After Atomizer merge |
-| Production stress tests | Not started | Before deployment |
-
-### Documentation Gaps
-
-| Document | Status |
-|----------|--------|
-| API reference | Partial (need docstrings) |
-| Training guide | ✅ Complete |
-| Integration guide | Needs writing |
-| User manual | Needs writing |
-| Video tutorials | Not started |
-
----
-
-## Part 7: Recommended Action Plan
-
-### This Week (Testing & Validation)
-
-```
-Day 1: Quick Validation
-├── Generate 10 Simple Beam variations in NX
-├── Parse all 10 cases
-└── Run 20-epoch training (30 min)
-    Goal: See loss decrease = network learns!
-
-Day 2-3: Expand Dataset
-├── Generate 50 variations with better coverage
-├── Include thickness, width, load, support variations
-└── Parse and organize train/val split (80/20)
-
-Day 4-5: Proper Training
-├── Train for 100 epochs with physics loss
-├── Monitor TensorBoard
-└── Validate on held-out cases
-    Goal: < 15% error on validation set
-```
-
-### Next 2 Weeks (Production Quality)
-
-```
-Week 1: Data & Training
-├── Generate 200+ training cases
-├── Train production model
-├── Run full test suite
-└── Document accuracy metrics
-
-Week 2: Integration Prep
-├── Create atomizer_field_bridge.py
-├── Add to Atomizer as submodule
-├── Test on existing optimization study
-└── Compare results vs pure FEA
-```
-
-### First Month (Full Integration)
-
-```
-Week 3-4:
-├── Full Atomizer integration
-├── Uncertainty-based FEA triggering
-├── Dashboard neural prediction tab
-├── Performance benchmarks
-
-Documentation:
-├── Integration guide
-├── Best practices
-├── Example workflows
-```
-
----
-
-## Conclusion
-
-### What You Have
-- ✅ Complete neural field learning system (~7,000 lines)
-- ✅ Physics-informed architecture
-- ✅ Validated pipeline (Simple Beam test passed)
-- ✅ Production-ready code structure
-- ✅ Comprehensive documentation
-
-### What You Need
-- ⏳ Training data (50-500 FEA cases)
-- ⏳ Trained model weights
-- ⏳ Atomizer integration code
-- ⏳ Production validation
-
-### The Key Insight
-
-**AtomizerField is not trying to replace FEA—it's learning FROM FEA to accelerate optimization.**
-
-The network encodes your engineering knowledge:
-- How forces propagate through structures
-- How geometry affects stress distribution
-- How boundary conditions constrain deformation
-
-Once trained, it can predict these patterns 1000× faster than computing them from scratch.
-
-### Next Concrete Step
-
-**Right now, today:**
-```bash
-# 1. Generate 10 Simple Beam variations in NX
-# 2. Parse them:
-python batch_parser.py --input ten_cases/ --output parsed_ten/
-
-# 3. Train for 20 epochs:
-python train.py --data_dirs parsed_ten/* --epochs 20
-
-# 4. Watch the loss decrease → Your network is learning physics!
-```
-
-This 2-hour test will prove the concept works. Then scale up.
-
----
-
-*Report generated: November 24, 2025*  
-*AtomizerField Version: 1.0*  
-*Status: Ready for Training Phase*

atomizer-field/COMPLETE_SUMMARY.md

@@ -1,567 +0,0 @@
-# AtomizerField - Complete Implementation Summary
-
-## ✅ What Has Been Built
-
-You now have a **complete, production-ready system** for neural field learning in structural optimization.
-
----
-
-## 📍 Location
-
-```
-c:\Users\antoi\Documents\Atomaste\Atomizer-Field\
-```
-
----
-
-## 📦 What's Inside (Complete File List)
-
-### Documentation (Read These!)
-```
-├── README.md                     # Phase 1 guide (parser)
-├── PHASE2_README.md              # Phase 2 guide (neural network)
-├── GETTING_STARTED.md            # Quick start tutorial
-├── SYSTEM_ARCHITECTURE.md        # System architecture (detailed!)
-├── COMPLETE_SUMMARY.md           # This file
-├── Context.md                    # Project vision
-└── Instructions.md               # Implementation spec
-```
-
-### Phase 1: Data Parser (✅ Implemented & Tested)
-```
-├── neural_field_parser.py        # Main parser: BDF/OP2 → Neural format
-├── validate_parsed_data.py       # Data validation
-├── batch_parser.py               # Batch processing
-└── metadata_template.json        # Design parameter template
-```
-
-### Phase 2: Neural Network (✅ Implemented & Tested)
-```
-├── neural_models/
-│   ├── __init__.py
-│   ├── field_predictor.py        # GNN (718K params) ✅ TESTED
-│   ├── physics_losses.py         # Loss functions ✅ TESTED
-│   └── data_loader.py            # Data pipeline ✅ TESTED
-│
-├── train.py                      # Training script
-└── predict.py                    # Inference script
-```
-
-### Configuration
-```
-└── requirements.txt              # All dependencies
-```
-
----
-
-## 🧪 Test Results
-
-### ✅ Phase 2 Neural Network Tests
-
-**1. GNN Model Test (field_predictor.py):**
-```
-Testing AtomizerField Model Creation...
-Model created: 718,221 parameters
-
-Test forward pass:
-  Displacement shape: torch.Size([100, 6])
-  Stress shape: torch.Size([100, 6])
-  Von Mises shape: torch.Size([100])
-
-Max values:
-  Max displacement: 3.249960
-  Max stress: 3.94
-
-Model test passed! ✓
-```
-
-**2. Loss Functions Test (physics_losses.py):**
-```
-Testing AtomizerField Loss Functions...
-
-Testing MSE loss...
-  Total loss: 3.885789 ✓
-
-Testing RELATIVE loss...
-  Total loss: 2.941448 ✓
-
-Testing PHYSICS loss...
-  Total loss: 3.850585 ✓
-  (All physics constraints working)
-
-Testing MAX loss...
-  Total loss: 20.127707 ✓
-
-Loss function tests passed! ✓
-```
-
-**Conclusion:** All neural network components working perfectly!
-
----
-
-## 🔍 How It Works - Visual Summary
-
-### The Big Picture
-
-```
-┌───────────────────────────────────────────────────────────┐
-│                    YOUR WORKFLOW                          │
-└───────────────────────────────────────────────────────────┘
-
-1️⃣ CREATE DESIGNS IN NX
-   ├─ Make 500 bracket variants
-   ├─ Different thicknesses, ribs, holes
-   └─ Run FEA on each → .bdf + .op2 files
-
-            ↓
-
-2️⃣ PARSE FEA DATA (Phase 1)
-   $ python batch_parser.py ./all_brackets
-
-   ├─ Converts 500 cases in ~2 hours
-   ├─ Output: neural_field_data.json + .h5
-   └─ Complete stress/displacement fields preserved
-
-            ↓
-
-3️⃣ TRAIN NEURAL NETWORK (Phase 2)
-   $ python train.py --train_dir brackets --epochs 150
-
-   ├─ Trains Graph Neural Network (GNN)
-   ├─ Learns physics of bracket behavior
-   ├─ Time: 8-12 hours (one-time!)
-   └─ Output: checkpoint_best.pt (3 MB)
-
-            ↓
-
-4️⃣ OPTIMIZE AT LIGHTNING SPEED
-   $ python predict.py --model checkpoint_best.pt --input new_design
-
-   ├─ Predicts in 15 milliseconds
-   ├─ Complete stress field (not just max!)
-   ├─ Test 10,000 designs in 2.5 minutes
-   └─ Find optimal design instantly!
-
-            ↓
-
-5️⃣ VERIFY & MANUFACTURE
-   ├─ Run full FEA on final design (verify accuracy)
-   └─ Manufacture optimal bracket
-```
-
----
-
-## 🎯 Key Innovation: Complete Fields
-
-### Old Way (Traditional Surrogate Models)
-```python
-# Only learns scalar values
-max_stress = neural_network(thickness, rib_height, hole_diameter)
-# Result: 450.2 MPa
-
-# Problems:
-❌ No spatial information
-❌ Can't see WHERE stress occurs
-❌ Can't guide design improvements
-❌ Black box optimization
-```
-
-### AtomizerField Way (Neural Field Learning)
-```python
-# Learns COMPLETE field at every point
-field_results = neural_network(mesh_graph)
-
-displacement = field_results['displacement']  # [15,432 nodes × 6 DOF]
-stress = field_results['stress']              # [15,432 nodes × 6 components]
-von_mises = field_results['von_mises']        # [15,432 nodes]
-
-# Now you know:
-✅ Max stress: 450.2 MPa
-✅ WHERE it occurs: Node 8,743 (near fillet)
-✅ Stress distribution across entire structure
-✅ Can intelligently add material where needed
-✅ Physics-guided optimization!
-```
-
----
-
-## 🧠 The Neural Network Architecture
-
-### What You Built
-
-```
-AtomizerFieldModel (718,221 parameters)
-
-INPUT:
-├─ Nodes: [x, y, z, BC_mask(6), loads(3)] → 12 features per node
-└─ Edges: [E, ν, ρ, G, α] → 5 features per edge (material)
-
-PROCESSING:
-├─ Node Encoder: 12 → 128 dimensions
-├─ Edge Encoder: 5 → 64 dimensions
-├─ Message Passing × 6 layers:
-│  ├─ Forces propagate through mesh
-│  ├─ Learns stiffness matrix behavior
-│  └─ Respects element connectivity
-│
-├─ Displacement Decoder: 128 → 6 (ux, uy, uz, θx, θy, θz)
-└─ Stress Predictor: displacement → stress tensor
-
-OUTPUT:
-├─ Displacement field at ALL nodes
-├─ Stress field at ALL elements
-└─ Von Mises stress everywhere
-```
-
-**Why This Works:**
-
-FEA solves: **K·u = f**
-- K = stiffness matrix (depends on mesh topology + materials)
-- u = displacement
-- f = forces
-
-Our GNN learns this relationship:
-- **Mesh topology** → Graph edges
-- **Materials** → Edge features
-- **BCs & loads** → Node features
-- **Message passing** → Mimics K·u = f solving!
-
-**Result:** Network learns physics, not just patterns!
-
----
-
-## 📊 Performance Benchmarks
-
-### Tested Performance
-
-| Component | Status | Performance |
-|-----------|--------|-------------|
-| GNN Forward Pass | ✅ Tested | 100 nodes in ~5ms |
-| Loss Functions | ✅ Tested | All 4 types working |
-| Data Pipeline | ✅ Implemented | Graph conversion ready |
-| Training Loop | ✅ Implemented | GPU-optimized |
-| Inference | ✅ Implemented | Batch prediction ready |
-
-### Expected Real-World Performance
-
-| Task | Traditional FEA | AtomizerField | Speedup |
-|------|----------------|---------------|---------|
-| 10k element model | 15 minutes | 5 ms | 180,000× |
-| 50k element model | 2 hours | 15 ms | 480,000× |
-| 100k element model | 8 hours | 35 ms | 823,000× |
-
-### Accuracy (Expected)
-
-| Metric | Target | Typical |
-|--------|--------|---------|
-| Displacement Error | < 5% | 2-3% |
-| Stress Error | < 10% | 5-8% |
-| Max Value Error | < 3% | 1-2% |
-
----
-
-## 🚀 How to Use (Step-by-Step)
-
-### Prerequisites
-
-1. **Python 3.8+** (you have Python 3.14)
-2. **NX Nastran** (you have it)
-3. **GPU recommended** for training (optional but faster)
-
-### Setup (One-Time)
-
-```bash
-# Navigate to project
-cd c:\Users\antoi\Documents\Atomaste\Atomizer-Field
-
-# Create virtual environment
-python -m venv atomizer_env
-
-# Activate
-atomizer_env\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-### Workflow
-
-#### Step 1: Generate FEA Data in NX
-
-```
-1. Create design in NX
-2. Mesh (CTETRA, CHEXA, CQUAD4, etc.)
-3. Apply materials (MAT1)
-4. Apply BCs (SPC)
-5. Apply loads (FORCE, PLOAD4)
-6. Run SOL 101 (Linear Static)
-7. Request: DISPLACEMENT=ALL, STRESS=ALL
-8. Get files: model.bdf, model.op2
-```
-
-#### Step 2: Parse FEA Results
-
-```bash
-# Organize files
-mkdir training_case_001
-mkdir training_case_001/input
-mkdir training_case_001/output
-cp your_model.bdf training_case_001/input/model.bdf
-cp your_model.op2 training_case_001/output/model.op2
-
-# Parse
-python neural_field_parser.py training_case_001
-
-# Validate
-python validate_parsed_data.py training_case_001
-
-# For many cases:
-python batch_parser.py ./all_your_cases
-```
-
-**Output:**
-- `neural_field_data.json` - Metadata (200 KB)
-- `neural_field_data.h5` - Fields (3 MB)
-
-#### Step 3: Train Neural Network
-
-```bash
-# Organize data
-mkdir training_data
-mkdir validation_data
-# Move 80% of parsed cases to training_data/
-# Move 20% of parsed cases to validation_data/
-
-# Train
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 100 \
-    --batch_size 4 \
-    --lr 0.001 \
-    --loss_type physics
-
-# Monitor (in another terminal)
-tensorboard --logdir runs/tensorboard
-```
-
-**Training takes:** 2-24 hours depending on dataset size
-
-**Output:**
-- `runs/checkpoint_best.pt` - Best model
-- `runs/config.json` - Configuration
-- `runs/tensorboard/` - Training logs
-
-#### Step 4: Run Predictions
-
-```bash
-# Single prediction
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input new_design_case \
-    --compare
-
-# Batch prediction
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input ./test_designs \
-    --batch \
-    --output_dir ./results
-```
-
-**Each prediction:** 5-50 milliseconds!
-
----
-
-## 📚 Data Format Details
-
-### Parsed Data Structure
-
-**JSON (neural_field_data.json):**
-- Metadata (version, timestamp, analysis type)
-- Mesh statistics (nodes, elements, types)
-- Materials (E, ν, ρ, G, α)
-- Boundary conditions (SPCs, MPCs)
-- Loads (forces, pressures, gravity)
-- Results summary (max values, units)
-
-**HDF5 (neural_field_data.h5):**
-- `/mesh/node_coordinates` - [N × 3] coordinates
-- `/results/displacement` - [N × 6] complete field
-- `/results/stress/*` - Complete stress tensors
-- `/results/strain/*` - Complete strain tensors
-- `/results/reactions` - Reaction forces
-
-**Why Two Files?**
-- JSON: Human-readable, metadata, structure
-- HDF5: Efficient, compressed, large arrays
-- Combined: Best of both worlds!
-
----
-
-## 🎓 What Makes This Special
-
-### 1. Physics-Informed Learning
-
-```python
-# Standard neural network
-loss = prediction_error
-
-# AtomizerField
-loss = prediction_error
-     + equilibrium_violation      # ∇·σ + f = 0
-     + constitutive_law_error      # σ = C:ε
-     + boundary_condition_violation # u = 0 at fixed nodes
-
-# Result: Learns physics, needs less data!
-```
-
-### 2. Graph Neural Networks
-
-```
-Traditional NN:
-Input → Dense Layers → Output
-(Ignores mesh structure!)
-
-AtomizerField GNN:
-Mesh Graph → Message Passing → Field Prediction
-(Respects topology, learns force flow!)
-```
-
-### 3. Complete Field Prediction
-
-```
-Traditional:
-- Only max stress
-- No spatial info
-- Black box
-
-AtomizerField:
-- Complete stress distribution
-- Know WHERE concentrations are
-- Physics-guided design
-```
-
----
-
-## 🔧 Troubleshooting
-
-### Common Issues
-
-**1. "No module named torch"**
-```bash
-pip install torch torch-geometric tensorboard
-```
-
-**2. "Out of memory during training"**
-```bash
-# Reduce batch size
-python train.py --batch_size 2
-
-# Or use smaller model
-python train.py --hidden_dim 64 --num_layers 4
-```
-
-**3. "Poor predictions"**
-- Need more training data (aim for 500+ cases)
-- Increase model size: `--hidden_dim 256 --num_layers 8`
-- Use physics loss: `--loss_type physics`
-- Ensure test cases within training distribution
-
-**4. NumPy warnings (like you saw)**
-- This is a Windows/NumPy compatibility issue
-- Doesn't affect functionality
-- Can be ignored or use specific NumPy version
-- The neural network components work perfectly (as tested!)
-
----
-
-## 📈 Next Steps
-
-### Immediate
-1. ✅ System is ready to use
-2. Generate training dataset (50-500 FEA cases)
-3. Parse with `batch_parser.py`
-4. Train first model with `train.py`
-5. Test predictions with `predict.py`
-
-### Short-term
-- Generate comprehensive dataset
-- Train production model
-- Validate accuracy on test set
-- Use for optimization!
-
-### Long-term (Phase 3+)
-- Nonlinear analysis support
-- Modal analysis
-- Thermal coupling
-- Atomizer dashboard integration
-- Cloud deployment
-
----
-
-## 📊 System Capabilities
-
-### What It Can Do
-
-✅ **Parse NX Nastran** - BDF/OP2 to neural format
-✅ **Handle Mixed Elements** - Solid, shell, beam
-✅ **Preserve Complete Fields** - All nodes/elements
-✅ **Graph Neural Networks** - Mesh-aware learning
-✅ **Physics-Informed** - Equilibrium, constitutive laws
-✅ **Fast Training** - GPU-accelerated, checkpointing
-✅ **Lightning Inference** - Millisecond predictions
-✅ **Batch Processing** - Handle hundreds of cases
-✅ **Validation** - Comprehensive quality checks
-✅ **Logging** - TensorBoard visualization
-
-### What It Delivers
-
-🎯 **1000× speedup** over traditional FEA
-🎯 **Complete field predictions** (not just max values)
-🎯 **Physics understanding** (know WHERE stress occurs)
-🎯 **Rapid optimization** (test millions of designs)
-🎯 **Production-ready** (error handling, documentation)
-
----
-
-## 🎉 Summary
-
-You now have a **complete, revolutionary system** for structural optimization:
-
-1. **Phase 1 Parser** - Converts FEA to ML format (✅ Implemented)
-2. **Phase 2 Neural Network** - Learns complete physics fields (✅ Implemented & Tested)
-3. **Training Pipeline** - GPU-optimized with checkpointing (✅ Implemented)
-4. **Inference Engine** - Millisecond predictions (✅ Implemented)
-5. **Documentation** - Comprehensive guides (✅ Complete)
-
-**Total:**
-- ~3,000 lines of production code
-- 7 documentation files
-- 8 Python modules
-- Complete testing
-- Ready for real-world use
-
-**Key Files to Read:**
-1. `GETTING_STARTED.md` - Quick tutorial
-2. `SYSTEM_ARCHITECTURE.md` - Detailed architecture
-3. `README.md` - Phase 1 guide
-4. `PHASE2_README.md` - Phase 2 guide
-
-**Start Here:**
-```bash
-cd c:\Users\antoi\Documents\Atomaste\Atomizer-Field
-# Read GETTING_STARTED.md
-# Generate your first training dataset
-# Train your first model!
-```
-
----
-
-**You're ready to revolutionize structural optimization! 🚀**
-
-From hours of FEA to milliseconds of prediction.
-From black-box optimization to physics-guided design.
-From scalar outputs to complete field understanding.
-
-**AtomizerField - The future of engineering optimization is here.**

atomizer-field/Context.md

@@ -1,127 +0,0 @@
-Context Instructions for Claude Sonnet 3.5
-Project: AtomizerField - Neural Field Learning for Structural Optimization
-System Context
-You are helping develop AtomizerField, a revolutionary branch of the Atomizer optimization platform that uses neural networks to learn and predict complete FEA field results (stress, displacement, strain at every node/element) instead of just scalar values. This enables 1000x faster optimization with physics understanding.
-Core Objective
-Transform structural optimization from black-box number crunching to intelligent, field-aware design exploration by training neural networks on complete FEA data, not just maximum values.
-Technical Foundation
-Current Stack:
-
-FEA: NX Nastran (BDF input, OP2/F06 output)
-Python Libraries: pyNastran, PyTorch, NumPy, H5PY
-Parent Project: Atomizer (optimization platform with dashboard)
-Data Format: Custom schema v1.0 for future-proof field storage
-
-Key Innovation:
-Instead of: parameters → FEA → max_stress (scalar)
-We learn: parameters → Neural Network → complete stress field (45,000 values)
-Project Structure
-AtomizerField/
-├── data_pipeline/
-│   ├── parser/           # BDF/OP2 to neural field format
-│   ├── generator/        # Automated FEA case generation
-│   └── validator/        # Data quality checks
-├── neural_models/
-│   ├── field_predictor/  # Core neural network
-│   ├── physics_layers/   # Physics-informed constraints
-│   └── training/         # Training scripts
-├── integration/
-│   └── atomizer_bridge/  # Integration with main Atomizer
-└── data/
-    └── training_cases/   # FEA data repository
-Current Development Phase
-Phase 1 (Current): Data Pipeline Development
-
-Parsing NX Nastran files (BDF/OP2) into training data
-Creating standardized data format
-Building automated case generation
-
-Next Phases:
-
-Phase 2: Neural network architecture
-Phase 3: Training pipeline
-Phase 4: Integration with Atomizer
-Phase 5: Production deployment
-
-Key Technical Concepts to Understand
-
-Field Learning: We're teaching NNs to predict stress/displacement at EVERY point in a structure, not just max values
-Physics-Informed: The NN must respect equilibrium, compatibility, and constitutive laws
-Graph Neural Networks: Mesh topology matters - we use GNNs to understand how forces flow through elements
-Transfer Learning: Knowledge from one project speeds up optimization on similar structures
-
-Code Style & Principles
-
-Future-Proof Data: All data structures versioned, backwards compatible
-Modular Design: Each component (parser, trainer, predictor) independent
-Validation First: Every data point validated for physics consistency
-Progressive Enhancement: Start simple (max stress), expand to fields
-Documentation: Every function documented with clear physics meaning
-
-Specific Instructions for Implementation
-When implementing code for AtomizerField:
-
-Always preserve field dimensionality - Don't reduce to scalars unless explicitly needed
-Use pyNastran's existing methods - Don't reinvent BDF/OP2 parsing
-Store data efficiently - HDF5 for arrays, JSON for metadata
-Validate physics - Check equilibrium, energy balance
-Think in fields - Visualize operations as field transformations
-Enable incremental learning - New data should improve existing models
-
-Current Task Context
-The user has:
-
-Set up NX Nastran analyses with full field outputs
-Generated BDF (input) and OP2 (output) files
-Needs to parse these into neural network training data
-
-The parser must:
-
-Extract complete mesh (nodes, elements, connectivity)
-Capture all boundary conditions and loads
-Store complete field results (not just max values)
-Maintain relationships between parameters and results
-Be robust to different element types (solid, shell, beam)
-
-Expected Outputs
-When asked about AtomizerField, provide:
-
-Practical, runnable code - No pseudocode unless requested
-Clear data flow - Show how data moves from FEA to NN
-Physics explanations - Why certain approaches work/fail
-Incremental steps - Break complex tasks into testable chunks
-Validation methods - How to verify data/model correctness
-
-Common Challenges & Solutions
-
-Large Data: Use HDF5 chunking and compression
-Mixed Element Types: Handle separately, combine for training
-Coordinate Systems: Always transform to global before storage
-Units: Standardize early (SI units recommended)
-Missing Data: Op2 might not have all requested fields - handle gracefully
-
-Integration Notes
-AtomizerField will eventually merge into main Atomizer:
-
-Keep interfaces clean and documented
-Use consistent data formats with Atomizer
-Prepare for dashboard visualization needs
-Enable both standalone and integrated operation
-
-Key Questions to Ask
-When implementing features, consider:
-
-Will this work with 1 million element meshes?
-Can we incrementally update models with new data?
-Does this respect physical laws?
-Is the data format forward-compatible?
-Can non-experts understand and use this?
-
-Ultimate Goal
-Create a system where engineers can:
-
-Run normal FEA analyses
-Automatically build neural surrogates from results
-Explore millions of designs instantly
-Understand WHY designs work through field visualization
-Optimize with physical insight, not blind search

atomizer-field/ENHANCEMENTS_GUIDE.md

@@ -1,494 +0,0 @@
-# AtomizerField Enhancements Guide
-
-## 🎯 What's Been Added (Phase 2.1)
-
-Following the review, I've implemented critical enhancements to make AtomizerField production-ready for real optimization workflows.
-
----
-
-## ✨ New Features
-
-### 1. **Optimization Interface** (`optimization_interface.py`)
-
-Direct integration with Atomizer optimization platform.
-
-**Key Features:**
-- Drop-in FEA replacement (1000× faster)
-- Gradient computation for sensitivity analysis
-- Batch evaluation (test 1000 designs in seconds)
-- Automatic performance tracking
-
-**Usage:**
-```python
-from optimization_interface import NeuralFieldOptimizer
-
-# Create optimizer
-optimizer = NeuralFieldOptimizer('checkpoint_best.pt')
-
-# Evaluate design
-results = optimizer.evaluate(graph_data)
-print(f"Max stress: {results['max_stress']:.2f} MPa")
-print(f"Time: {results['inference_time_ms']:.1f} ms")
-
-# Get gradients for optimization
-gradients = optimizer.get_sensitivities(graph_data, objective='max_stress')
-
-# Update design using gradients (much faster than finite differences!)
-new_parameters = parameters - learning_rate * gradients['node_gradients']
-```
-
-**Benefits:**
-- **Gradient-based optimization** - Use analytical gradients instead of finite differences
-- **Field-aware optimization** - Know WHERE to add/remove material
-- **Performance tracking** - Monitor speedup vs traditional FEA
-
-### 2. **Uncertainty Quantification** (`neural_models/uncertainty.py`)
-
-Know when to trust predictions and when to run FEA!
-
-**Key Features:**
-- Ensemble-based uncertainty estimation
-- Confidence intervals for predictions
-- Automatic FEA recommendation
-- Online learning from new FEA results
-
-**Usage:**
-```python
-from neural_models.uncertainty import UncertainFieldPredictor
-
-# Create ensemble (5 models)
-ensemble = UncertainFieldPredictor(model_config, n_ensemble=5)
-
-# Get predictions with uncertainty
-predictions = ensemble(graph_data, return_uncertainty=True)
-
-# Check if FEA validation needed
-recommendation = ensemble.needs_fea_validation(predictions, threshold=0.1)
-
-if recommendation['recommend_fea']:
-    print("Run FEA - prediction uncertain")
-    run_full_fea()
-else:
-    print("Trust neural prediction - high confidence!")
-    use_neural_result()
-```
-
-**Benefits:**
-- **Risk management** - Know when predictions are reliable
-- **Adaptive workflow** - Use FEA only when needed
-- **Cost optimization** - Minimize expensive FEA runs
-
-### 3. **Configuration System** (`atomizer_field_config.yaml`)
-
-Long-term vision configuration for all features.
-
-**Key Sections:**
-- Model architecture (foundation models, adaptation layers)
-- Training (progressive, online learning, physics loss weights)
-- Data pipeline (normalization, augmentation, multi-resolution)
-- Optimization (gradients, uncertainty, FEA fallback)
-- Deployment (versioning, production settings)
-- Integration (Atomizer dashboard, API)
-
-**Usage:**
-```yaml
-# Enable foundation model transfer learning
-model:
-  foundation:
-    enabled: true
-    path: "models/physics_foundation_v1.pt"
-    freeze: true
-
-# Enable online learning during optimization
-training:
-  online:
-    enabled: true
-    update_frequency: 10
-```
-
-### 4. **Online Learning** (in `uncertainty.py`)
-
-Learn from FEA runs during optimization.
-
-**Workflow:**
-```python
-from neural_models.uncertainty import OnlineLearner
-
-# Create learner
-learner = OnlineLearner(model, learning_rate=0.0001)
-
-# During optimization:
-for design in optimization_loop:
-    # Fast neural prediction
-    result = model.predict(design)
-
-    # If high uncertainty, run FEA
-    if uncertainty > threshold:
-        fea_result = run_fea(design)
-
-        # Learn from it!
-        learner.add_fea_result(design, fea_result)
-
-        # Quick update (10 gradient steps)
-        if len(learner.replay_buffer) >= 10:
-            learner.quick_update(steps=10)
-
-    # Model gets better over time!
-```
-
-**Benefits:**
-- **Continuous improvement** - Model learns during optimization
-- **Less FEA needed** - Model adapts to current design space
-- **Virtuous cycle** - Better predictions → less FEA → faster optimization
-
----
-
-## 🚀 Complete Workflow Examples
-
-### Example 1: Basic Optimization
-
-```python
-# 1. Load trained model
-from optimization_interface import NeuralFieldOptimizer
-
-optimizer = NeuralFieldOptimizer('runs/checkpoint_best.pt')
-
-# 2. Evaluate 1000 designs
-results = []
-for design_params in design_space:
-    # Generate mesh
-    graph_data = create_mesh(design_params)
-
-    # Predict in milliseconds
-    pred = optimizer.evaluate(graph_data)
-
-    results.append({
-        'params': design_params,
-        'max_stress': pred['max_stress'],
-        'max_displacement': pred['max_displacement']
-    })
-
-# 3. Find best design
-best = min(results, key=lambda r: r['max_stress'])
-print(f"Optimal design: {best['params']}")
-print(f"Stress: {best['max_stress']:.2f} MPa")
-
-# 4. Validate with FEA
-fea_validation = run_fea(best['params'])
-```
-
-**Time:** 1000 designs in ~30 seconds (vs 3000 hours FEA!)
-
-### Example 2: Uncertainty-Guided Optimization
-
-```python
-from neural_models.uncertainty import UncertainFieldPredictor, OnlineLearner
-
-# 1. Create ensemble
-ensemble = UncertainFieldPredictor(model_config, n_ensemble=5)
-learner = OnlineLearner(ensemble.models[0])
-
-# 2. Optimization with smart FEA usage
-fea_count = 0
-
-for iteration in range(1000):
-    design = generate_candidate()
-
-    # Predict with uncertainty
-    pred = ensemble(design, return_uncertainty=True)
-
-    # Check if we need FEA
-    rec = ensemble.needs_fea_validation(pred, threshold=0.1)
-
-    if rec['recommend_fea']:
-        # High uncertainty - run FEA
-        fea_result = run_fea(design)
-        fea_count += 1
-
-        # Learn from it
-        learner.add_fea_result(design, fea_result)
-
-        # Update model every 10 FEA runs
-        if fea_count % 10 == 0:
-            learner.quick_update(steps=10)
-
-        # Use FEA result
-        result = fea_result
-    else:
-        # Low uncertainty - trust neural prediction
-        result = pred
-
-    # Continue optimization...
-
-print(f"Total FEA runs: {fea_count}/1000")
-print(f"FEA reduction: {(1 - fea_count/1000)*100:.1f}%")
-```
-
-**Result:** ~10-20 FEA runs instead of 1000 (98% reduction!)
-
-### Example 3: Gradient-Based Optimization
-
-```python
-from optimization_interface import NeuralFieldOptimizer
-import torch
-
-# 1. Initialize
-optimizer = NeuralFieldOptimizer('checkpoint_best.pt', enable_gradients=True)
-
-# 2. Starting design
-parameters = torch.tensor([2.5, 5.0, 15.0], requires_grad=True)  # thickness, radius, height
-
-# 3. Gradient-based optimization loop
-learning_rate = 0.1
-
-for step in range(100):
-    # Convert parameters to mesh
-    graph_data = parameters_to_mesh(parameters)
-
-    # Evaluate
-    result = optimizer.evaluate(graph_data)
-    stress = result['max_stress']
-
-    # Get sensitivities
-    grads = optimizer.get_sensitivities(graph_data, objective='max_stress')
-
-    # Update parameters (gradient descent)
-    with torch.no_grad():
-        parameters -= learning_rate * torch.tensor(grads['node_gradients'].mean(axis=0))
-
-    if step % 10 == 0:
-        print(f"Step {step}: Stress = {stress:.2f} MPa")
-
-print(f"Final design: {parameters.tolist()}")
-print(f"Final stress: {stress:.2f} MPa")
-```
-
-**Benefits:**
-- Uses analytical gradients (exact!)
-- Much faster than finite differences
-- Finds optimal designs quickly
-
----
-
-## 📊 Performance Improvements
-
-### With New Features:
-
-| Capability | Before | After |
-|-----------|--------|-------|
-| **Optimization** | Finite differences | Analytical gradients (10× faster) |
-| **Reliability** | No uncertainty info | Confidence intervals, FEA recommendations |
-| **Adaptivity** | Fixed model | Online learning during optimization |
-| **Integration** | Manual | Clean API for Atomizer |
-
-### Expected Workflow Performance:
-
-**Optimize 1000-design bracket study:**
-
-| Step | Traditional | With AtomizerField | Speedup |
-|------|-------------|-------------------|---------|
-| Generate designs | 1 day | 1 day | 1× |
-| Evaluate (FEA) | 3000 hours | 30 seconds (neural) | 360,000× |
-| + Validation (20 FEA) | - | 40 hours | - |
-| **Total** | **125 days** | **2 days** | **62× faster** |
-
----
-
-## 🔧 Implementation Priority
-
-### ✅ Phase 2.1 (Complete - Just Added)
-1. ✅ Optimization interface with gradients
-2. ✅ Uncertainty quantification with ensemble
-3. ✅ Online learning capability
-4. ✅ Configuration system
-5. ✅ Complete documentation
-
-### 📅 Phase 2.2 (Next Steps)
-1. Multi-resolution training (coarse → fine)
-2. Foundation model architecture
-3. Parameter encoding improvements
-4. Advanced data augmentation
-
-### 📅 Phase 3 (Future)
-1. Atomizer dashboard integration
-2. REST API deployment
-3. Real-time field visualization
-4. Cloud deployment
-
----
-
-## 📁 Updated File Structure
-
-```
-Atomizer-Field/
-│
-├── 🆕 optimization_interface.py    # NEW: Optimization API
-├── 🆕 atomizer_field_config.yaml   # NEW: Configuration system
-│
-├── neural_models/
-│   ├── field_predictor.py
-│   ├── physics_losses.py
-│   ├── data_loader.py
-│   └── 🆕 uncertainty.py           # NEW: Uncertainty & online learning
-│
-├── train.py
-├── predict.py
-├── neural_field_parser.py
-├── validate_parsed_data.py
-├── batch_parser.py
-│
-└── Documentation/
-    ├── README.md
-    ├── PHASE2_README.md
-    ├── GETTING_STARTED.md
-    ├── SYSTEM_ARCHITECTURE.md
-    ├── COMPLETE_SUMMARY.md
-    └── 🆕 ENHANCEMENTS_GUIDE.md     # NEW: This file
-```
-
----
-
-## 🎓 How to Use the Enhancements
-
-### Step 1: Basic Optimization (No Uncertainty)
-
-```bash
-# Use optimization interface for fast evaluation
-python -c "
-from optimization_interface import NeuralFieldOptimizer
-opt = NeuralFieldOptimizer('checkpoint_best.pt')
-# Evaluate designs...
-"
-```
-
-### Step 2: Add Uncertainty Quantification
-
-```bash
-# Train ensemble (5 models with different initializations)
-python train.py --ensemble 5 --epochs 100
-
-# Use ensemble for predictions with confidence
-python -c "
-from neural_models.uncertainty import UncertainFieldPredictor
-ensemble = UncertainFieldPredictor(config, n_ensemble=5)
-# Get predictions with uncertainty...
-"
-```
-
-### Step 3: Enable Online Learning
-
-```bash
-# During optimization, update model from FEA runs
-# See Example 2 above for complete code
-```
-
-### Step 4: Customize via Config
-
-```bash
-# Edit atomizer_field_config.yaml
-# Enable features you want:
-#   - Foundation models
-#   - Online learning
-#   - Multi-resolution
-#   - Etc.
-
-# Train with config
-python train.py --config atomizer_field_config.yaml
-```
-
----
-
-## 🎯 Key Benefits Summary
-
-### 1. **Faster Optimization**
-- Analytical gradients instead of finite differences
-- Batch evaluation (1000 designs/minute)
-- 10-100× faster than before
-
-### 2. **Smarter Workflow**
-- Know when to trust predictions (uncertainty)
-- Automatic FEA recommendation
-- Adaptive FEA usage (98% reduction)
-
-### 3. **Continuous Improvement**
-- Model learns during optimization
-- Less FEA needed over time
-- Better predictions on current design space
-
-### 4. **Production Ready**
-- Clean API for integration
-- Configuration management
-- Performance monitoring
-- Comprehensive documentation
-
----
-
-## 🚦 Getting Started with Enhancements
-
-### Quick Start:
-
-```python
-# 1. Use optimization interface (simplest)
-from optimization_interface import create_optimizer
-
-opt = create_optimizer('checkpoint_best.pt')
-result = opt.evaluate(graph_data)
-
-# 2. Add uncertainty (recommended)
-from neural_models.uncertainty import create_uncertain_predictor
-
-ensemble = create_uncertain_predictor(model_config, n_ensemble=5)
-pred = ensemble(graph_data, return_uncertainty=True)
-
-if pred['stress_rel_uncertainty'] > 0.1:
-    print("High uncertainty - recommend FEA")
-
-# 3. Enable online learning (advanced)
-from neural_models.uncertainty import OnlineLearner
-
-learner = OnlineLearner(model)
-# Learn from FEA during optimization...
-```
-
-### Full Integration:
-
-See examples above for complete workflows integrating:
-- Optimization interface
-- Uncertainty quantification
-- Online learning
-- Configuration management
-
----
-
-## 📚 Additional Resources
-
-**Documentation:**
-- [GETTING_STARTED.md](GETTING_STARTED.md) - Basic tutorial
-- [SYSTEM_ARCHITECTURE.md](SYSTEM_ARCHITECTURE.md) - System details
-- [PHASE2_README.md](PHASE2_README.md) - Neural network guide
-
-**Code Examples:**
-- `optimization_interface.py` - See `if __name__ == "__main__"` section
-- `uncertainty.py` - See usage examples at bottom
-
-**Configuration:**
-- `atomizer_field_config.yaml` - All configuration options
-
----
-
-## 🎉 Summary
-
-**Phase 2.1 adds four critical capabilities:**
-
-1. ✅ **Optimization Interface** - Easy integration with Atomizer
-2. ✅ **Uncertainty Quantification** - Know when to trust predictions
-3. ✅ **Online Learning** - Improve during optimization
-4. ✅ **Configuration System** - Manage all features
-
-**Result:** Production-ready neural field learning system that's:
-- Fast (1000× speedup)
-- Smart (uncertainty-aware)
-- Adaptive (learns during use)
-- Integrated (ready for Atomizer)
-
-**You're ready to revolutionize structural optimization!** 🚀

atomizer-field/ENVIRONMENT_SETUP.md

@@ -1,419 +0,0 @@
-# AtomizerField Environment Setup
-
-## ✅ Problem Solved!
-
-The NumPy MINGW-W64 segmentation fault issue has been resolved by creating a proper conda environment with compatible packages.
-
----
-
-## Solution Summary
-
-**Issue:** NumPy built with MINGW-W64 on Windows caused segmentation faults when importing
-
-**Solution:** Created conda environment `atomizer_field` with properly compiled NumPy from conda-forge
-
-**Result:** ✅ All tests passing! System ready for use.
-
----
-
-## Environment Details
-
-### Conda Environment: `atomizer_field`
-
-**Created with:**
-```bash
-conda create -n atomizer_field python=3.10 numpy scipy -y
-conda activate atomizer_field
-conda install pytorch torchvision torchaudio cpuonly -c pytorch -y
-pip install torch-geometric pyNastran h5py tensorboard
-```
-
-### Installed Packages:
-
-**Core Scientific:**
-- Python 3.10.19
-- NumPy 1.26.4 (conda-compiled, no MINGW-W64 issues!)
-- SciPy 1.15.3
-- Matplotlib 3.10.7
-
-**PyTorch Stack:**
-- PyTorch 2.5.1 (CPU)
-- TorchVision 0.20.1
-- TorchAudio 2.5.1
-- PyTorch Geometric 2.7.0
-
-**AtomizerField Dependencies:**
-- pyNastran 1.4.1
-- H5Py 3.15.1
-- TensorBoard 2.20.0
-
-**Total Environment Size:** ~2GB
-
----
-
-## Usage
-
-### Activate Environment
-
-```bash
-# Windows (PowerShell)
-conda activate atomizer_field
-
-# Windows (Command Prompt)
-activate atomizer_field
-
-# Linux/Mac
-conda activate atomizer_field
-```
-
-### Run Tests
-
-```bash
-# Activate environment
-conda activate atomizer_field
-
-# Quick smoke tests (30 seconds)
-python test_suite.py --quick
-
-# Physics validation (15 minutes)
-python test_suite.py --physics
-
-# Full test suite (1 hour)
-python test_suite.py --full
-
-# Test with Simple Beam
-python test_simple_beam.py
-```
-
-### Run AtomizerField
-
-```bash
-# Activate environment
-conda activate atomizer_field
-
-# Parse FEA data
-python neural_field_parser.py path/to/case
-
-# Train model
-python train.py --data_dirs case1 case2 case3 --epochs 100
-
-# Make predictions
-python predict.py --model best_model.pt --data test_case
-```
-
----
-
-## Test Results
-
-### First Successful Test Run
-
-```
-============================================================
-AtomizerField Test Suite v1.0
-Mode: QUICK
-============================================================
-
-PHASE 1: SMOKE TESTS (5 minutes)
-============================================================
-
-[TEST] Model Creation
-  Description: Verify GNN model can be instantiated
-    Creating GNN model...
-    Model created: 128,589 parameters
-  Status: [PASS]
-  Duration: 0.06s
-
-[TEST] Forward Pass
-  Description: Verify model can process dummy data
-    Testing forward pass...
-    Displacement shape: torch.Size([100, 6]) [OK]
-    Stress shape: torch.Size([100, 6]) [OK]
-    Von Mises shape: torch.Size([100]) [OK]
-  Status: [PASS]
-  Duration: 0.02s
-
-[TEST] Loss Computation
-  Description: Verify loss functions work
-    Testing loss functions...
-    MSE loss: 4.027361 [OK]
-    RELATIVE loss: 3.027167 [OK]
-    PHYSICS loss: 3.659333 [OK]
-    MAX loss: 13.615703 [OK]
-  Status: [PASS]
-  Duration: 0.00s
-
-============================================================
-TEST SUMMARY
-============================================================
-
-Total Tests: 3
-  + Passed: 3
-  - Failed: 0
-  Pass Rate: 100.0%
-
-[SUCCESS] ALL TESTS PASSED - SYSTEM READY!
-============================================================
-
-Total testing time: 0.0 minutes
-```
-
-**Status:** ✅ All smoke tests passing!
-
----
-
-## Environment Management
-
-### View Environment Info
-
-```bash
-# List all conda environments
-conda env list
-
-# View installed packages
-conda activate atomizer_field
-conda list
-```
-
-### Update Packages
-
-```bash
-conda activate atomizer_field
-
-# Update conda packages
-conda update numpy scipy pytorch
-
-# Update pip packages
-pip install --upgrade torch-geometric pyNastran h5py tensorboard
-```
-
-### Export Environment
-
-```bash
-# Export for reproducibility
-conda activate atomizer_field
-conda env export > environment.yml
-
-# Recreate from export
-conda env create -f environment.yml
-```
-
-### Remove Environment (if needed)
-
-```bash
-# Deactivate first
-conda deactivate
-
-# Remove environment
-conda env remove -n atomizer_field
-```
-
----
-
-## Troubleshooting
-
-### Issue: conda command not found
-
-**Solution:** Add conda to PATH or use Anaconda Prompt
-
-### Issue: Import errors
-
-**Solution:** Make sure environment is activated
-```bash
-conda activate atomizer_field
-```
-
-### Issue: CUDA/GPU not available
-
-**Note:** Current installation is CPU-only. For GPU support:
-```bash
-conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
-```
-
-### Issue: Slow training
-
-**Solutions:**
-1. Use GPU (see above)
-2. Reduce batch size
-3. Reduce model size (hidden_dim)
-4. Use fewer training epochs
-
----
-
-## Performance Comparison
-
-### Before (pip-installed NumPy):
-```
-Error: Segmentation fault (core dumped)
-CRASHES ARE TO BE EXPECTED
-```
-
-### After (conda environment):
-```
-✅ All tests passing
-✅ Model creates successfully (128,589 parameters)
-✅ Forward pass working
-✅ All 4 loss functions operational
-✅ No crashes or errors
-```
-
----
-
-## Next Steps
-
-### 1. Run Full Test Suite
-
-```bash
-conda activate atomizer_field
-
-# Run all smoke tests
-python test_suite.py --quick
-
-# Run physics tests
-python test_suite.py --physics
-
-# Run complete validation
-python test_suite.py --full
-```
-
-### 2. Test with Simple Beam
-
-```bash
-conda activate atomizer_field
-python test_simple_beam.py
-```
-
-Expected output:
-- Files found ✓
-- Test case setup ✓
-- Modules imported ✓
-- Beam parsed ✓
-- Data validated ✓
-- Graph created ✓
-- Prediction made ✓
-
-### 3. Generate Training Data
-
-```bash
-# Parse multiple FEA cases
-conda activate atomizer_field
-python batch_parser.py --input Models/ --output training_data/
-```
-
-### 4. Train Model
-
-```bash
-conda activate atomizer_field
-
-python train.py \
-  --data_dirs training_data/* \
-  --epochs 100 \
-  --batch_size 16 \
-  --lr 0.001 \
-  --loss physics
-
-# Monitor with TensorBoard
-tensorboard --logdir runs/
-```
-
-### 5. Make Predictions
-
-```bash
-conda activate atomizer_field
-
-python predict.py \
-  --model checkpoints/best_model.pt \
-  --data test_case/ \
-  --output predictions/
-```
-
----
-
-## Environment Specifications
-
-### System Requirements
-
-**Minimum:**
-- CPU: 4 cores
-- RAM: 8GB
-- Disk: 5GB free space
-- OS: Windows 10/11, Linux, macOS
-
-**Recommended:**
-- CPU: 8+ cores
-- RAM: 16GB+
-- Disk: 20GB+ free space
-- GPU: NVIDIA with 8GB+ VRAM (optional)
-
-### Installation Time
-
-- Conda environment creation: ~5 minutes
-- Package downloads: ~10 minutes
-- Total setup time: ~15 minutes
-
-### Disk Usage
-
-```
-atomizer_field environment: ~2GB
-  - Python: ~200MB
-  - PyTorch: ~800MB
-  - NumPy/SciPy: ~400MB
-  - Other packages: ~600MB
-
-Training data (per case): ~1-10MB
-Model checkpoint: ~500KB-2MB
-Test results: <1MB
-```
-
----
-
-## Success Checklist
-
-### Environment Setup ✅
-- [x] Conda installed
-- [x] Environment `atomizer_field` created
-- [x] All packages installed
-- [x] No MINGW-W64 errors
-- [x] Tests running successfully
-
-### System Validation ✅
-- [x] Model creation works (128K params)
-- [x] Forward pass functional
-- [x] All loss functions operational
-- [x] Batch processing works
-- [x] Gradient flow correct
-
-### Ready for Production ✅
-- [x] Smoke tests pass
-- [ ] Physics tests pass (requires training)
-- [ ] Learning tests pass (requires training)
-- [ ] Integration tests pass (requires training data)
-
----
-
-## Summary
-
-**✅ Environment successfully configured!**
-
-**What's Working:**
-- Conda environment `atomizer_field` created
-- NumPy MINGW-W64 issue resolved
-- All smoke tests passing (3/3)
-- Model creates and runs correctly
-- 128,589 parameters instantiated
-- All 4 loss functions working
-
-**What's Next:**
-1. Run full test suite
-2. Test with Simple Beam model
-3. Generate training data (50-500 cases)
-4. Train neural network
-5. Validate performance
-6. Deploy to production
-
-**The system is now ready for training and deployment!** 🚀
-
----
-
-*Environment Setup v1.0 - Problem Solved!*
-*Conda environment: atomizer_field*
-*All tests passing - System ready for use*

atomizer-field/FINAL_IMPLEMENTATION_REPORT.md

@@ -1,531 +0,0 @@
-# AtomizerField - Final Implementation Report
-
-## Executive Summary
-
-**Project:** AtomizerField Neural Field Learning System
-**Version:** 2.1
-**Status:** ✅ Production-Ready
-**Date:** 2024
-
----
-
-## 🎯 Mission Accomplished
-
-You asked for **Phase 2** (neural network training).
-
-**I delivered a complete, production-ready neural field learning platform with advanced optimization capabilities.**
-
----
-
-## 📦 Complete Deliverables
-
-### Phase 1: Data Parser (4 files)
-1. ✅ `neural_field_parser.py` (650 lines)
-2. ✅ `validate_parsed_data.py` (400 lines)
-3. ✅ `batch_parser.py` (350 lines)
-4. ✅ `metadata_template.json`
-
-### Phase 2: Neural Network (5 files)
-5. ✅ `neural_models/field_predictor.py` (490 lines) **[TESTED ✓]**
-6. ✅ `neural_models/physics_losses.py` (450 lines) **[TESTED ✓]**
-7. ✅ `neural_models/data_loader.py` (420 lines)
-8. ✅ `train.py` (430 lines)
-9. ✅ `predict.py` (380 lines)
-
-### Phase 2.1: Advanced Features (3 files) **[NEW!]**
-10. ✅ `optimization_interface.py` (430 lines)
-11. ✅ `neural_models/uncertainty.py` (380 lines)
-12. ✅ `atomizer_field_config.yaml` (configuration system)
-
-### Documentation (8 files)
-13. ✅ `README.md` (Phase 1 guide)
-14. ✅ `PHASE2_README.md` (Phase 2 guide)
-15. ✅ `GETTING_STARTED.md` (Quick start)
-16. ✅ `SYSTEM_ARCHITECTURE.md` (Complete architecture)
-17. ✅ `COMPLETE_SUMMARY.md` (Implementation summary)
-18. ✅ `ENHANCEMENTS_GUIDE.md` (Phase 2.1 features)
-19. ✅ `FINAL_IMPLEMENTATION_REPORT.md` (This file)
-20. Context.md, Instructions.md (Original specs)
-
-**Total:** 20 files, ~4,500 lines of production code
-
----
-
-## 🧪 Testing & Validation
-
-### ✅ Successfully Tested:
-
-**1. Graph Neural Network (field_predictor.py)**
-```
-✓ Model creation: 718,221 parameters
-✓ Forward pass: Displacement [100, 6]
-✓ Forward pass: Stress [100, 6]
-✓ Forward pass: Von Mises [100]
-✓ Max values extraction working
-```
-
-**2. Physics-Informed Loss Functions (physics_losses.py)**
-```
-✓ MSE Loss: Working
-✓ Relative Loss: Working
-✓ Physics-Informed Loss: Working (all 4 components)
-✓ Max Value Loss: Working
-```
-
-**3. All Components Validated**
-- Graph construction logic ✓
-- Data pipeline architecture ✓
-- Training loop ✓
-- Inference engine ✓
-- Optimization interface ✓
-- Uncertainty quantification ✓
-
----
-
-## 🎯 Key Innovations Implemented
-
-### 1. Complete Field Learning
-**Not just max values - entire stress/displacement distributions!**
-
-```
-Traditional: max_stress = 450 MPa (1 number)
-AtomizerField: stress_field[15,432 nodes × 6 components] (92,592 values!)
-```
-
-**Benefit:** Know WHERE stress concentrations occur, not just maximum value
-
-### 2. Graph Neural Networks
-**Respects mesh topology - learns how forces flow through structure**
-
-```
-6 message passing layers
-Forces propagate through connected elements
-Learns physics, not just patterns
-```
-
-**Benefit:** Understands structural mechanics, needs less training data
-
-### 3. Physics-Informed Training
-**Enforces physical laws during learning**
-
-```python
-Loss = Data_Loss (match FEA)
-     + Equilibrium_Loss (∇·σ + f = 0)
-     + Constitutive_Loss (σ = C:ε)
-     + Boundary_Condition_Loss (u = 0 at fixed nodes)
-```
-
-**Benefit:** Better generalization, faster convergence, physically plausible predictions
-
-### 4. Optimization Interface
-**Drop-in replacement for FEA with gradients!**
-
-```python
-# Traditional finite differences
-for i in range(n_params):
-    params[i] += delta
-    stress_plus = fea(params)  # 2 hours
-    params[i] -= 2*delta
-    stress_minus = fea(params)  # 2 hours
-    gradient[i] = (stress_plus - stress_minus) / (2*delta)
-# Total: 4n hours for n parameters
-
-# AtomizerField analytical gradients
-gradients = optimizer.get_sensitivities(graph_data)  # 15 milliseconds!
-# Total: 15 ms (960,000× faster!)
-```
-
-**Benefit:** Gradient-based optimization 1,000,000× faster than finite differences
-
-### 5. Uncertainty Quantification
-**Know when to trust predictions**
-
-```python
-ensemble = UncertainFieldPredictor(config, n_ensemble=5)
-predictions = ensemble(design, return_uncertainty=True)
-
-if predictions['stress_rel_uncertainty'] > 0.1:
-    result = run_fea(design)  # High uncertainty - use FEA
-else:
-    result = predictions  # Low uncertainty - trust neural network
-```
-
-**Benefit:** Intelligent FEA usage - only run when needed (98% reduction possible)
-
-### 6. Online Learning
-**Model improves during optimization**
-
-```python
-learner = OnlineLearner(model)
-
-for design in optimization:
-    pred = model.predict(design)
-
-    if high_uncertainty:
-        fea_result = run_fea(design)
-        learner.add_fea_result(design, fea_result)
-        learner.quick_update()  # Model learns!
-```
-
-**Benefit:** Model adapts to current design space, needs less FEA over time
-
----
-
-## 📊 Performance Metrics
-
-### Speed (Tested on Similar Architectures)
-
-| Model Size | FEA Time | Neural Time | Speedup |
-|-----------|----------|-------------|---------|
-| 10k elements | 15 min | 5 ms | **180,000×** |
-| 50k elements | 2 hours | 15 ms | **480,000×** |
-| 100k elements | 8 hours | 35 ms | **823,000×** |
-
-### Accuracy (Expected Based on Literature)
-
-| Metric | Target | Typical |
-|--------|--------|---------|
-| Displacement Error | < 5% | 2-3% |
-| Stress Error | < 10% | 5-8% |
-| Max Value Error | < 3% | 1-2% |
-
-### Training Requirements
-
-| Dataset Size | Training Time | Epochs | Hardware |
-|-------------|--------------|--------|----------|
-| 100 cases | 2-4 hours | 100 | RTX 3080 |
-| 500 cases | 8-12 hours | 150 | RTX 3080 |
-| 1000 cases | 24-48 hours | 200 | RTX 3080 |
-
----
-
-## 🚀 What This Enables
-
-### Before AtomizerField:
-```
-Optimize bracket:
-├─ Test 10 designs per week (FEA limited)
-├─ Only know max_stress values
-├─ No spatial understanding
-├─ Blind optimization (try random changes)
-└─ Total time: Months
-
-Cost: $50,000 in engineering time
-```
-
-### With AtomizerField:
-```
-Optimize bracket:
-├─ Generate 500 training variants → Run FEA once (2 weeks)
-├─ Train model once → 8 hours
-├─ Test 1,000,000 designs → 2.5 hours
-├─ Know complete stress fields everywhere
-├─ Physics-guided optimization (know WHERE to reinforce)
-└─ Total time: 3 weeks
-
-Cost: $5,000 in engineering time (10× reduction!)
-```
-
-### Real-World Example:
-
-**Optimize aircraft bracket (100,000 element model):**
-
-| Method | Designs Tested | Time | Cost |
-|--------|---------------|------|------|
-| Traditional FEA | 10 | 80 hours | $8,000 |
-| AtomizerField | 1,000,000 | 72 hours | $5,000 |
-| **Improvement** | **100,000× more** | **Similar time** | **40% cheaper** |
-
----
-
-## 💡 Use Cases
-
-### 1. Rapid Design Exploration
-```
-Test thousands of variants in minutes
-Identify promising design regions
-Focus FEA on final validation
-```
-
-### 2. Real-Time Optimization
-```
-Interactive design tool
-Engineer modifies geometry
-Instant stress prediction (15 ms)
-Immediate feedback
-```
-
-### 3. Physics-Guided Design
-```
-Complete stress field shows:
-- WHERE stress concentrations occur
-- HOW to add material efficiently
-- WHY design fails or succeeds
-→ Intelligent design improvements
-```
-
-### 4. Multi-Objective Optimization
-```
-Optimize for:
-- Minimize weight
-- Minimize max stress
-- Minimize max displacement
-- Minimize cost
-→ Explore Pareto frontier rapidly
-```
-
----
-
-## 🏗️ System Architecture Summary
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                   COMPLETE SYSTEM FLOW                       │
-└─────────────────────────────────────────────────────────────┘
-
-1. GENERATE FEA DATA (NX Nastran)
-   ├─ Design variants (thickness, ribs, holes, etc.)
-   ├─ Run SOL 101 → .bdf + .op2 files
-   └─ Time: Days to weeks (one-time cost)
-
-2. PARSE TO NEURAL FORMAT (Phase 1)
-   ├─ batch_parser.py → Process all cases
-   ├─ Extract complete fields (not just max values!)
-   └─ Output: JSON + HDF5 format
-          Time: ~15 seconds per case
-
-3. TRAIN NEURAL NETWORK (Phase 2)
-   ├─ data_loader.py → Convert to graphs
-   ├─ train.py → Train GNN with physics loss
-   ├─ TensorBoard monitoring
-   └─ Output: checkpoint_best.pt
-          Time: 8-12 hours (one-time)
-
-4. OPTIMIZE WITH CONFIDENCE (Phase 2.1)
-   ├─ optimization_interface.py → Fast evaluation
-   ├─ uncertainty.py → Know when to trust
-   ├─ Online learning → Improve during use
-   └─ Result: Optimal design!
-          Time: Minutes to hours
-
-5. VALIDATE & MANUFACTURE
-   ├─ Run FEA on final design (verify)
-   └─ Manufacture optimal part
-```
-
----
-
-## 📁 Repository Structure
-
-```
-c:\Users\antoi\Documents\Atomaste\Atomizer-Field\
-│
-├── 📄 Documentation (8 files)
-│   ├── FINAL_IMPLEMENTATION_REPORT.md  ← YOU ARE HERE
-│   ├── ENHANCEMENTS_GUIDE.md          ← Phase 2.1 features
-│   ├── COMPLETE_SUMMARY.md            ← Quick overview
-│   ├── GETTING_STARTED.md             ← Start here!
-│   ├── SYSTEM_ARCHITECTURE.md         ← Deep dive
-│   ├── README.md                      ← Phase 1 guide
-│   ├── PHASE2_README.md               ← Phase 2 guide
-│   └── Context.md, Instructions.md    ← Vision & specs
-│
-├── 🔧 Phase 1: Parser (4 files)
-│   ├── neural_field_parser.py
-│   ├── validate_parsed_data.py
-│   ├── batch_parser.py
-│   └── metadata_template.json
-│
-├── 🧠 Phase 2: Neural Network (5 files)
-│   ├── neural_models/
-│   │   ├── field_predictor.py     [TESTED ✓]
-│   │   ├── physics_losses.py      [TESTED ✓]
-│   │   ├── data_loader.py
-│   │   └── uncertainty.py         [NEW!]
-│   ├── train.py
-│   └── predict.py
-│
-├── 🚀 Phase 2.1: Optimization (2 files)
-│   ├── optimization_interface.py   [NEW!]
-│   └── atomizer_field_config.yaml [NEW!]
-│
-├── 📦 Configuration
-│   └── requirements.txt
-│
-└── 🔬 Example Data
-    └── Models/Simple Beam/
-```
-
----
-
-## ✅ Quality Assurance
-
-### Code Quality
-- ✅ Production-ready error handling
-- ✅ Comprehensive docstrings
-- ✅ Type hints where appropriate
-- ✅ Modular, extensible design
-- ✅ Configuration management
-
-### Testing
-- ✅ Neural network components tested
-- ✅ Loss functions validated
-- ✅ Architecture verified
-- ✅ Ready for real-world use
-
-### Documentation
-- ✅ 8 comprehensive guides
-- ✅ Code examples throughout
-- ✅ Troubleshooting sections
-- ✅ Usage tutorials
-- ✅ Architecture explanations
-
----
-
-## 🎓 Knowledge Transfer
-
-### To Use This System:
-
-**1. Read Documentation (30 minutes)**
-```
-Start → GETTING_STARTED.md
-Deep dive → SYSTEM_ARCHITECTURE.md
-Features → ENHANCEMENTS_GUIDE.md
-```
-
-**2. Generate Training Data (1-2 weeks)**
-```
-Create designs in NX → Run FEA → Parse with batch_parser.py
-Aim for 500+ cases for production use
-```
-
-**3. Train Model (8-12 hours)**
-```
-python train.py --train_dir training_data --val_dir validation_data
-Monitor with TensorBoard
-Save best checkpoint
-```
-
-**4. Optimize (minutes to hours)**
-```
-Use optimization_interface.py for fast evaluation
-Enable uncertainty for smart FEA usage
-Online learning for continuous improvement
-```
-
-### Skills Required:
-- ✅ Python programming (intermediate)
-- ✅ NX Nastran (create FEA models)
-- ✅ Basic neural networks (helpful but not required)
-- ✅ Structural mechanics (understand results)
-
----
-
-## 🔮 Future Roadmap
-
-### Phase 3: Atomizer Integration
-- Dashboard visualization of stress fields
-- Database integration
-- REST API for predictions
-- Multi-user support
-
-### Phase 4: Advanced Analysis
-- Nonlinear analysis (plasticity, large deformation)
-- Contact and friction
-- Composite materials
-- Modal analysis (natural frequencies)
-
-### Phase 5: Foundation Models
-- Pre-trained physics foundation
-- Transfer learning across component types
-- Multi-resolution architecture
-- Universal structural predictor
-
----
-
-## 💰 Business Value
-
-### Return on Investment
-
-**Initial Investment:**
-- Engineering time: 2-3 weeks
-- Compute (GPU training): ~$50
-- Total: ~$10,000
-
-**Returns:**
-- 1000× faster optimization
-- 10-100× more designs tested
-- Better final designs (physics-guided)
-- Reduced prototyping costs
-- Faster time-to-market
-
-**Payback Period:** First major optimization project
-
-### Competitive Advantage
-- Explore design spaces competitors can't reach
-- Find optimal designs faster
-- Reduce development costs
-- Accelerate innovation
-
----
-
-## 🎉 Final Summary
-
-### What You Have:
-
-**A complete, production-ready neural field learning system that:**
-
-1. ✅ Parses NX Nastran FEA results into ML format
-2. ✅ Trains Graph Neural Networks with physics constraints
-3. ✅ Predicts complete stress/displacement fields 1000× faster than FEA
-4. ✅ Provides optimization interface with analytical gradients
-5. ✅ Quantifies prediction uncertainty for smart FEA usage
-6. ✅ Learns online during optimization
-7. ✅ Includes comprehensive documentation and examples
-
-### Implementation Stats:
-
-- **Files:** 20 (12 code, 8 documentation)
-- **Lines of Code:** ~4,500
-- **Test Status:** Core components validated ✓
-- **Documentation:** Complete ✓
-- **Production Ready:** Yes ✓
-
-### Key Capabilities:
-
-| Capability | Status |
-|-----------|--------|
-| Complete field prediction | ✅ Implemented |
-| Graph neural networks | ✅ Implemented & Tested |
-| Physics-informed loss | ✅ Implemented & Tested |
-| Fast training pipeline | ✅ Implemented |
-| Fast inference | ✅ Implemented |
-| Optimization interface | ✅ Implemented |
-| Uncertainty quantification | ✅ Implemented |
-| Online learning | ✅ Implemented |
-| Configuration management | ✅ Implemented |
-| Complete documentation | ✅ Complete |
-
----
-
-## 🚀 You're Ready!
-
-**Next Steps:**
-
-1. ✅ Read `GETTING_STARTED.md`
-2. ✅ Generate your training dataset (50-500 FEA cases)
-3. ✅ Train your first model
-4. ✅ Run predictions and compare with FEA
-5. ✅ Start optimizing 1000× faster!
-
-**The future of structural optimization is in your hands.**
-
-**AtomizerField - Transform hours of FEA into milliseconds of prediction!** 🎯
-
----
-
-*Implementation completed with comprehensive testing, documentation, and advanced features. Ready for production deployment.*
-
-**Version:** 2.1
-**Status:** Production-Ready ✅
-**Date:** 2024

atomizer-field/GETTING_STARTED.md

@@ -1,327 +0,0 @@
-# AtomizerField - Getting Started Guide
-
-Welcome to AtomizerField! This guide will get you up and running with neural field learning for structural optimization.
-
-## Overview
-
-AtomizerField transforms structural optimization from hours-per-design to milliseconds-per-design by using Graph Neural Networks to predict complete FEA field results.
-
-### The Two-Phase Approach
-
-```
-Phase 1: Data Pipeline
-NX Nastran Files → Parser → Neural Field Format
-
-Phase 2: Neural Network Training
-Neural Field Data → GNN Training → Fast Field Predictor
-```
-
-## Installation
-
-### Prerequisites
-- Python 3.8 or higher
-- NX Nastran (for generating FEA data)
-- NVIDIA GPU (recommended for Phase 2 training)
-
-### Setup
-
-```bash
-# Clone or navigate to project directory
-cd Atomizer-Field
-
-# Create virtual environment
-python -m venv atomizer_env
-
-# Activate environment
-# On Windows:
-atomizer_env\Scripts\activate
-# On Linux/Mac:
-source atomizer_env/bin/activate
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-## Phase 1: Parse Your FEA Data
-
-### Step 1: Generate FEA Results in NX
-
-1. Create your model in NX
-2. Generate mesh
-3. Apply materials, BCs, and loads
-4. Run **SOL 101** (Linear Static)
-5. Request output: `DISPLACEMENT=ALL`, `STRESS=ALL`, `STRAIN=ALL`
-6. Ensure these files are generated:
-   - `model.bdf` (input deck)
-   - `model.op2` (results)
-
-### Step 2: Organize Files
-
-```bash
-mkdir training_case_001
-mkdir training_case_001/input
-mkdir training_case_001/output
-
-# Copy files
-cp your_model.bdf training_case_001/input/model.bdf
-cp your_model.op2 training_case_001/output/model.op2
-```
-
-### Step 3: Parse
-
-```bash
-# Single case
-python neural_field_parser.py training_case_001
-
-# Validate
-python validate_parsed_data.py training_case_001
-
-# Batch processing (for multiple cases)
-python batch_parser.py ./all_training_cases
-```
-
-**Output:**
-- `neural_field_data.json` - Metadata
-- `neural_field_data.h5` - Field data
-
-See [README.md](README.md) for detailed Phase 1 documentation.
-
-## Phase 2: Train Neural Network
-
-### Step 1: Prepare Dataset
-
-You need:
-- **Minimum:** 50-100 parsed FEA cases
-- **Recommended:** 500+ cases for production use
-- **Variation:** Different geometries, loads, BCs
-
-Organize into train/val splits (80/20):
-
-```bash
-mkdir training_data
-mkdir validation_data
-
-# Move 80% of cases to training_data/
-# Move 20% of cases to validation_data/
-```
-
-### Step 2: Train Model
-
-```bash
-# Basic training
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 100 \
-    --batch_size 4
-
-# Monitor progress
-tensorboard --logdir runs/tensorboard
-```
-
-Training will:
-- Create checkpoints in `runs/`
-- Log metrics to TensorBoard
-- Save best model as `checkpoint_best.pt`
-
-**Expected Time:** 2-24 hours depending on dataset size and GPU.
-
-### Step 3: Run Inference
-
-```bash
-# Predict on new case
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input test_case_001 \
-    --compare
-
-# Batch prediction
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input ./test_cases \
-    --batch
-```
-
-**Result:** 5-50 milliseconds per prediction!
-
-See [PHASE2_README.md](PHASE2_README.md) for detailed Phase 2 documentation.
-
-## Typical Workflow
-
-### For Development (Learning the System)
-
-```bash
-# 1. Parse a few test cases
-python batch_parser.py ./test_cases
-
-# 2. Quick training test (small dataset)
-python train.py \
-    --train_dir ./test_cases \
-    --val_dir ./test_cases \
-    --epochs 10 \
-    --batch_size 2
-
-# 3. Test inference
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input test_cases/case_001
-```
-
-### For Production (Real Optimization)
-
-```bash
-# 1. Generate comprehensive training dataset
-#    - Vary all design parameters
-#    - Include diverse loading conditions
-#    - Cover full design space
-
-# 2. Parse all cases
-python batch_parser.py ./all_fea_cases
-
-# 3. Split into train/val
-# Use script or manually organize
-
-# 4. Train production model
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 200 \
-    --batch_size 8 \
-    --hidden_dim 256 \
-    --num_layers 8 \
-    --loss_type physics
-
-# 5. Validate on held-out test set
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input ./test_data \
-    --batch \
-    --compare
-
-# 6. Use for optimization!
-```
-
-## Key Files Reference
-
-| File | Purpose |
-|------|---------|
-| **Phase 1** | |
-| `neural_field_parser.py` | Parse NX Nastran to neural field format |
-| `validate_parsed_data.py` | Validate parsed data quality |
-| `batch_parser.py` | Batch process multiple cases |
-| `metadata_template.json` | Template for design parameters |
-| **Phase 2** | |
-| `train.py` | Train GNN model |
-| `predict.py` | Run inference on trained model |
-| `neural_models/field_predictor.py` | GNN architecture |
-| `neural_models/physics_losses.py` | Loss functions |
-| `neural_models/data_loader.py` | Data pipeline |
-| **Documentation** | |
-| `README.md` | Phase 1 detailed guide |
-| `PHASE2_README.md` | Phase 2 detailed guide |
-| `Context.md` | Project vision and architecture |
-| `Instructions.md` | Original implementation spec |
-
-## Common Issues & Solutions
-
-### "No cases found"
-- Check directory structure: `case_dir/input/model.bdf` and `case_dir/output/model.op2`
-- Ensure files are named exactly `model.bdf` and `model.op2`
-
-### "Out of memory during training"
-- Reduce `--batch_size` (try 2 or 1)
-- Use smaller model: `--hidden_dim 64 --num_layers 4`
-- Process larger models in chunks
-
-### "Poor prediction accuracy"
-- Need more training data (aim for 500+ cases)
-- Increase model capacity: `--hidden_dim 256 --num_layers 8`
-- Use physics-informed loss: `--loss_type physics`
-- Check if test case is within training distribution
-
-### "Training loss not decreasing"
-- Lower learning rate: `--lr 0.0001`
-- Check data normalization (should be automatic)
-- Start with simple MSE loss: `--loss_type mse`
-
-## Example: End-to-End Workflow
-
-Let's say you want to optimize a bracket design:
-
-```bash
-# 1. Generate 100 bracket variants in NX with different:
-#    - Wall thicknesses (1-5mm)
-#    - Rib heights (5-20mm)
-#    - Hole diameters (6-12mm)
-#    - Run FEA on each
-
-# 2. Parse all variants
-python batch_parser.py ./bracket_variants
-
-# 3. Split dataset
-# training_data: 80 cases
-# validation_data: 20 cases
-
-# 4. Train model
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 150 \
-    --batch_size 4 \
-    --output_dir ./bracket_model
-
-# 5. Test model (after training completes)
-python predict.py \
-    --model bracket_model/checkpoint_best.pt \
-    --input new_bracket_design \
-    --compare
-
-# 6. Optimize: Generate 10,000 design variants
-#    Predict in seconds instead of weeks!
-for design in design_space:
-    results = predict(design)
-    if results['max_stress'] < 300 and results['weight'] < optimal:
-        optimal = design
-```
-
-## Next Steps
-
-1. **Start Small:** Parse 5-10 test cases, train small model
-2. **Validate:** Compare predictions with FEA ground truth
-3. **Scale Up:** Gradually increase dataset size
-4. **Production:** Train final model on comprehensive dataset
-5. **Optimize:** Use trained model for rapid design exploration
-
-## Resources
-
-- **Phase 1 Detailed Docs:** [README.md](README.md)
-- **Phase 2 Detailed Docs:** [PHASE2_README.md](PHASE2_README.md)
-- **Project Context:** [Context.md](Context.md)
-- **Example Data:** Check `Models/` folder
-
-## Getting Help
-
-If you encounter issues:
-
-1. Check documentation (README.md, PHASE2_README.md)
-2. Verify file structure and naming
-3. Review error messages carefully
-4. Test with smaller dataset first
-5. Check GPU memory and batch size
-
-## Success Metrics
-
-You'll know it's working when:
-
-- ✓ Parser processes cases without errors
-- ✓ Validation shows no critical issues
-- ✓ Training loss decreases steadily
-- ✓ Validation loss follows training loss
-- ✓ Predictions are within 5-10% of FEA
-- ✓ Inference takes milliseconds
-
----
-
-**Ready to revolutionize your optimization workflow?**
-
-Start with Phase 1 parsing, then move to Phase 2 training. Within days, you'll have a neural network that predicts FEA results 1000x faster!

atomizer-field/IMPLEMENTATION_STATUS.md

@@ -1,500 +0,0 @@
-# AtomizerField Implementation Status
-
-## Project Overview
-
-**AtomizerField** is a neural field learning system that replaces FEA simulations with graph neural networks for 1000× faster structural optimization.
-
-**Key Innovation:** Learn complete stress/displacement FIELDS (45,000+ values per simulation) instead of just scalar maximum values, enabling full field predictions with neural networks.
-
----
-
-## Implementation Status: ✅ COMPLETE
-
-All phases of AtomizerField have been implemented and are ready for use.
-
----
-
-## Phase 1: Data Parser ✅ COMPLETE
-
-**Purpose:** Convert NX Nastran FEA results into neural field training data
-
-### Implemented Files:
-
-1. **neural_field_parser.py** (650 lines)
-   - Main BDF/OP2 parser
-   - Extracts complete mesh, materials, BCs, loads
-   - Exports full displacement and stress fields
-   - HDF5 + JSON output format
-   - Status: ✅ Tested and working
-
-2. **validate_parsed_data.py** (400 lines)
-   - Data quality validation
-   - Physics consistency checks
-   - Comprehensive reporting
-   - Status: ✅ Tested and working
-
-3. **batch_parser.py** (350 lines)
-   - Process multiple FEA cases
-   - Parallel processing support
-   - Batch statistics and reporting
-   - Status: ✅ Ready for use
-
-**Total:** ~1,400 lines for complete data pipeline
-
----
-
-## Phase 2: Neural Network ✅ COMPLETE
-
-**Purpose:** Graph neural network architecture for field prediction
-
-### Implemented Files:
-
-1. **neural_models/field_predictor.py** (490 lines)
-   - GNN architecture: 718,221 parameters
-   - 6 message passing layers
-   - Predicts displacement (6 DOF) and stress (6 components)
-   - Custom MeshGraphConv for FEA topology
-   - Status: ✅ Tested - model creates and runs
-
-2. **neural_models/physics_losses.py** (450 lines)
-   - 4 loss function types:
-     - MSE Loss
-     - Relative Loss
-     - Physics-Informed Loss (equilibrium, constitutive, BC)
-     - Max Error Loss
-   - Status: ✅ Tested - all losses compute correctly
-
-3. **neural_models/data_loader.py** (420 lines)
-   - PyTorch Geometric dataset
-   - Graph construction from mesh
-   - Feature engineering (12D nodes, 5D edges)
-   - Batch processing
-   - Status: ✅ Tested and working
-
-4. **train.py** (430 lines)
-   - Complete training pipeline
-   - TensorBoard integration
-   - Checkpointing and early stopping
-   - Command-line interface
-   - Status: ✅ Ready for training
-
-5. **predict.py** (380 lines)
-   - Fast inference engine (5-50ms)
-   - Batch prediction
-   - Ground truth comparison
-   - Status: ✅ Ready for use
-
-**Total:** ~2,170 lines for complete neural pipeline
-
----
-
-## Phase 2.1: Advanced Features ✅ COMPLETE
-
-**Purpose:** Optimization interface, uncertainty quantification, online learning
-
-### Implemented Files:
-
-1. **optimization_interface.py** (430 lines)
-   - Drop-in FEA replacement for Atomizer
-   - Analytical gradient computation (1M× faster than FD)
-   - Fast evaluation (15ms per design)
-   - Design parameter encoding
-   - Status: ✅ Ready for integration
-
-2. **neural_models/uncertainty.py** (380 lines)
-   - Ensemble-based uncertainty (5 models)
-   - Automatic FEA validation recommendations
-   - Online learning from new FEA runs
-   - Confidence-based model updates
-   - Status: ✅ Ready for use
-
-3. **atomizer_field_config.yaml**
-   - YAML configuration system
-   - Foundation models
-   - Progressive training
-   - Online learning settings
-   - Status: ✅ Complete
-
-**Total:** ~810 lines for advanced features
-
----
-
-## Phase 3: Testing Framework ✅ COMPLETE
-
-**Purpose:** Comprehensive validation from basic functionality to production
-
-### Master Orchestrator:
-
-**test_suite.py** (403 lines)
-- Four testing modes: --quick, --physics, --learning, --full
-- 18 comprehensive tests
-- JSON results export
-- Progress tracking and reporting
-- Status: ✅ Complete and ready
-
-### Test Modules:
-
-1. **tests/test_synthetic.py** (297 lines)
-   - 5 smoke tests
-   - Model creation, forward pass, losses, batch, gradients
-   - Status: ✅ Complete
-
-2. **tests/test_physics.py** (370 lines)
-   - 4 physics validation tests
-   - Cantilever analytical, equilibrium, energy, constitutive law
-   - Compares with known solutions
-   - Status: ✅ Complete
-
-3. **tests/test_learning.py** (410 lines)
-   - 4 learning capability tests
-   - Memorization, interpolation, extrapolation, pattern recognition
-   - Demonstrates learning with synthetic data
-   - Status: ✅ Complete
-
-4. **tests/test_predictions.py** (400 lines)
-   - 5 integration tests
-   - Parser, training, accuracy, performance, batch inference
-   - Complete pipeline validation
-   - Status: ✅ Complete
-
-5. **tests/analytical_cases.py** (450 lines)
-   - Library of 5 analytical solutions
-   - Cantilever, simply supported, tension, pressure vessel, torsion
-   - Ground truth for validation
-   - Status: ✅ Complete
-
-6. **test_simple_beam.py** (377 lines)
-   - 7-step integration test
-   - Tests with user's actual Simple Beam model
-   - Complete pipeline: parse → validate → graph → predict
-   - Status: ✅ Complete
-
-**Total:** ~2,700 lines of comprehensive testing
-
----
-
-## Documentation ✅ COMPLETE
-
-### Implementation Guides:
-
-1. **README.md** - Project overview and quick start
-2. **PHASE2_README.md** - Neural network documentation
-3. **GETTING_STARTED.md** - Step-by-step usage guide
-4. **SYSTEM_ARCHITECTURE.md** - Technical architecture
-5. **COMPLETE_SUMMARY.md** - Comprehensive system summary
-6. **ENHANCEMENTS_GUIDE.md** - Phase 2.1 features guide
-7. **FINAL_IMPLEMENTATION_REPORT.md** - Implementation report
-8. **TESTING_FRAMEWORK_SUMMARY.md** - Testing overview
-9. **TESTING_COMPLETE.md** - Complete testing documentation
-10. **IMPLEMENTATION_STATUS.md** - This file
-
-**Total:** 10 comprehensive documentation files
-
----
-
-## Project Statistics
-
-### Code Implementation:
-```
-Phase 1 (Data Parser):        ~1,400 lines
-Phase 2 (Neural Network):     ~2,170 lines
-Phase 2.1 (Advanced Features): ~810 lines
-Phase 3 (Testing):            ~2,700 lines
-────────────────────────────────────────
-Total Implementation:         ~7,080 lines
-```
-
-### Test Coverage:
-```
-Smoke tests:        5 tests
-Physics tests:      4 tests
-Learning tests:     4 tests
-Integration tests:  5 tests
-Simple Beam test:   7 steps
-────────────────────────────
-Total:             18 tests + integration
-```
-
-### File Count:
-```
-Core Implementation:  12 files
-Test Modules:         6 files
-Documentation:       10 files
-Configuration:        3 files
-────────────────────────────
-Total:               31 files
-```
-
----
-
-## What Works Right Now
-
-### ✅ Data Pipeline
-- Parse BDF/OP2 files → Working
-- Extract mesh, materials, BCs, loads → Working
-- Export full displacement/stress fields → Working
-- Validate data quality → Working
-- Batch processing → Working
-
-### ✅ Neural Network
-- Create GNN model (718K params) → Working
-- Forward pass (displacement + stress) → Working
-- All 4 loss functions → Working
-- Batch processing → Working
-- Gradient flow → Working
-
-### ✅ Advanced Features
-- Optimization interface → Implemented
-- Uncertainty quantification → Implemented
-- Online learning → Implemented
-- Configuration system → Implemented
-
-### ✅ Testing
-- All test modules → Complete
-- Test orchestrator → Complete
-- Analytical library → Complete
-- Simple Beam test → Complete
-
----
-
-## Ready to Use
-
-### Immediate Usage (Environment Fixed):
-
-1. **Parse FEA Data:**
-   ```bash
-   python neural_field_parser.py path/to/case_directory
-   ```
-
-2. **Validate Parsed Data:**
-   ```bash
-   python validate_parsed_data.py path/to/case_directory
-   ```
-
-3. **Run Tests:**
-   ```bash
-   python test_suite.py --quick
-   python test_simple_beam.py
-   ```
-
-4. **Train Model:**
-   ```bash
-   python train.py --data_dirs case1 case2 case3 --epochs 100
-   ```
-
-5. **Make Predictions:**
-   ```bash
-   python predict.py --model checkpoints/best_model.pt --data test_case
-   ```
-
-6. **Optimize with Atomizer:**
-   ```python
-   from optimization_interface import NeuralFieldOptimizer
-   optimizer = NeuralFieldOptimizer('best_model.pt')
-   results = optimizer.evaluate(design_graph)
-   ```
-
----
-
-## Current Limitation
-
-### NumPy Environment Issue
-- **Issue:** MINGW-W64 NumPy on Windows causes segmentation faults
-- **Impact:** Cannot run tests that import NumPy (most tests)
-- **Workaround Options:**
-  1. Use conda environment: `conda install numpy`
-  2. Use WSL (Windows Subsystem for Linux)
-  3. Run on native Linux system
-  4. Wait for NumPy Windows compatibility improvement
-
-**All code is complete and ready to run once environment is fixed.**
-
----
-
-## Production Readiness Checklist
-
-### Pre-Training ✅
-- [x] Data parser implemented
-- [x] Neural architecture implemented
-- [x] Loss functions implemented
-- [x] Training pipeline implemented
-- [x] Testing framework implemented
-- [x] Documentation complete
-
-### For Training ⏳
-- [ ] Resolve NumPy environment issue
-- [ ] Generate 50-500 training cases
-- [ ] Run training pipeline
-- [ ] Validate physics compliance
-- [ ] Benchmark performance
-
-### For Production ⏳
-- [ ] Train on diverse design space
-- [ ] Validate < 10% prediction error
-- [ ] Demonstrate 1000× speedup
-- [ ] Integrate with Atomizer
-- [ ] Deploy uncertainty quantification
-- [ ] Enable online learning
-
----
-
-## Next Actions
-
-### Immediate (Once Environment Fixed):
-1. Run smoke tests: `python test_suite.py --quick`
-2. Test Simple Beam: `python test_simple_beam.py`
-3. Verify all tests pass
-
-### Short Term (Training Phase):
-1. Generate diverse training dataset (50-500 cases)
-2. Parse all cases: `python batch_parser.py`
-3. Train model: `python train.py --full`
-4. Validate physics: `python test_suite.py --physics`
-5. Check performance: `python test_suite.py --full`
-
-### Medium Term (Integration):
-1. Integrate with Atomizer optimization loop
-2. Test on real design optimization
-3. Validate vs FEA ground truth
-4. Deploy uncertainty quantification
-5. Enable online learning
-
----
-
-## Key Technical Achievements
-
-### Architecture
-✅ Graph Neural Network respects mesh topology
-✅ Physics-informed loss functions enforce constraints
-✅ 718,221 parameters for complex field learning
-✅ 6 message passing layers for information propagation
-
-### Performance
-✅ Target: 1000× speedup vs FEA (5-50ms inference)
-✅ Batch processing for optimization loops
-✅ Analytical gradients for fast sensitivity analysis
-
-### Innovation
-✅ Complete field learning (not just max values)
-✅ Uncertainty quantification for confidence
-✅ Online learning during optimization
-✅ Drop-in FEA replacement interface
-
-### Validation
-✅ 18 comprehensive tests
-✅ Analytical solutions for ground truth
-✅ Physics compliance verification
-✅ Learning capability confirmation
-
----
-
-## System Capabilities
-
-### What AtomizerField Can Do:
-
-1. **Parse FEA Results**
-   - Read Nastran BDF/OP2 files
-   - Extract complete mesh and results
-   - Export to neural format
-
-2. **Learn from FEA**
-   - Train on 50-500 examples
-   - Learn complete displacement/stress fields
-   - Generalize to new designs
-
-3. **Fast Predictions**
-   - 5-50ms inference (vs 30-300s FEA)
-   - 1000× speedup
-   - Batch processing capability
-
-4. **Optimization Integration**
-   - Drop-in FEA replacement
-   - Analytical gradients
-   - 1M× faster sensitivity analysis
-
-5. **Quality Assurance**
-   - Uncertainty quantification
-   - Automatic FEA validation triggers
-   - Online learning improvements
-
-6. **Physics Compliance**
-   - Equilibrium enforcement
-   - Constitutive law compliance
-   - Boundary condition respect
-   - Energy conservation
-
----
-
-## Success Metrics
-
-### Code Quality
-- ✅ ~7,000 lines of production code
-- ✅ Comprehensive error handling
-- ✅ Extensive documentation
-- ✅ Modular architecture
-
-### Testing
-- ✅ 18 automated tests
-- ✅ Progressive validation strategy
-- ✅ Analytical ground truth
-- ✅ Performance benchmarks
-
-### Features
-- ✅ Complete data pipeline
-- ✅ Neural architecture
-- ✅ Training infrastructure
-- ✅ Optimization interface
-- ✅ Uncertainty quantification
-- ✅ Online learning
-
-### Documentation
-- ✅ 10 comprehensive guides
-- ✅ Code examples
-- ✅ Usage instructions
-- ✅ Architecture details
-
----
-
-## Conclusion
-
-**AtomizerField is fully implemented and ready for training and deployment.**
-
-### Completed:
-- ✅ All phases implemented (Phase 1, 2, 2.1, 3)
-- ✅ ~7,000 lines of production code
-- ✅ 18 comprehensive tests
-- ✅ 10 documentation files
-- ✅ Complete testing framework
-
-### Remaining:
-- ⏳ Resolve NumPy environment issue
-- ⏳ Generate training dataset
-- ⏳ Train and validate model
-- ⏳ Deploy to production
-
-### Ready to:
-1. Run tests (once environment fixed)
-2. Train on FEA data
-3. Make predictions 1000× faster
-4. Integrate with Atomizer
-5. Enable online learning
-
-**The system is production-ready pending training data and environment setup.** 🚀
-
----
-
-## Contact & Support
-
-- **Project:** AtomizerField Neural Field Learning System
-- **Purpose:** 1000× faster FEA predictions for structural optimization
-- **Status:** Implementation complete, ready for training
-- **Documentation:** See 10 comprehensive guides in project root
-
-**AtomizerField is ready to revolutionize structural optimization with neural field learning!**
-
----
-
-*Implementation Status Report*
-*Version: 1.0 - Complete*
-*Date: January 2025*
-*Total Implementation: ~7,000 lines across 31 files*

atomizer-field/Instructions.md

@@ -1,674 +0,0 @@
-Neural Field Data Parser: From NX Nastran Files to Training Data
-Complete Implementation Guide
-
-What You Have vs What You Need
-✅ What NX Nastran Gives You:
-Files Available:
-
-.sim - Simulation file with load/BC definitions
-.fem - Finite element model
-.prt - Part geometry
-.bdf/.dat - Nastran input deck (mesh, materials, loads, BCs)
-.op2 - Binary results (stress, displacement, strain)
-.f06 - ASCII results (human readable)
-.log - Solver log
-
-This is SUFFICIENT! The BDF contains everything about setup, OP2 contains all results.
-
-Step-by-Step Instructions for Manual Data Generation
-Step 1: Set Up Your Analysis in NX
-1. Create your geometry in NX
-2. Generate mesh (record statistics)
-3. Apply materials
-4. Define boundary conditions:
-   - Fixed supports
-   - Pinned constraints
-   - Contact (if needed)
-5. Apply loads:
-   - Forces
-   - Pressures
-   - Gravity
-6. Set up solution parameters
-7. Run analysis
-8. Ensure these files are generated:
-   - model.bdf (or .dat)
-   - model.op2
-   - model.f06
-Step 2: Organize Your Files
-training_case_001/
-├── input/
-│   ├── model.bdf          # Main input deck
-│   ├── model.sim          # NX simulation file
-│   └── geometry.prt       # Original geometry
-├── output/
-│   ├── model.op2          # Binary results
-│   ├── model.f06          # ASCII results
-│   └── model.log          # Solver log
-└── metadata.json          # Your manual annotations
-
-Python Parser Implementation
-Main Parser Script
-python"""
-neural_field_parser.py
-Parses NX Nastran files into Neural Field training data
-"""
-
-import json
-import numpy as np
-import h5py
-from pathlib import Path
-from datetime import datetime
-import hashlib
-
-# pyNastran imports
-from pyNastran.bdf.bdf import BDF
-from pyNastran.op2.op2 import OP2
-
-class NastranToNeuralFieldParser:
-    """
-    Parses Nastran BDF/OP2 files into Neural Field data structure
-    """
-    
-    def __init__(self, case_directory):
-        self.case_dir = Path(case_directory)
-        self.bdf_file = self.case_dir / "input" / "model.bdf"
-        self.op2_file = self.case_dir / "output" / "model.op2"
-        
-        # Initialize readers
-        self.bdf = BDF(debug=False)
-        self.op2 = OP2(debug=False)
-        
-        # Data structure
-        self.neural_field_data = {
-            "metadata": {},
-            "geometry": {},
-            "mesh": {},
-            "materials": {},
-            "boundary_conditions": {},
-            "loads": {},
-            "results": {}
-        }
-        
-    def parse_all(self):
-        """
-        Main parsing function
-        """
-        print("Starting parse of Nastran files...")
-        
-        # Parse input deck
-        print("Reading BDF file...")
-        self.bdf.read_bdf(str(self.bdf_file))
-        
-        # Parse results
-        print("Reading OP2 file...")
-        self.op2.read_op2(str(self.op2_file))
-        
-        # Extract all data
-        self.extract_metadata()
-        self.extract_mesh()
-        self.extract_materials()
-        self.extract_boundary_conditions()
-        self.extract_loads()
-        self.extract_results()
-        
-        # Save to file
-        self.save_data()
-        
-        print("Parse complete!")
-        return self.neural_field_data
-    
-    def extract_metadata(self):
-        """
-        Extract metadata and analysis info
-        """
-        self.neural_field_data["metadata"] = {
-            "version": "1.0.0",
-            "created_at": datetime.now().isoformat(),
-            "source": "NX_Nastran",
-            "case_directory": str(self.case_dir),
-            "analysis_type": self.op2.sol,  # SOL 101, 103, etc.
-            "title": self.bdf.case_control_deck.title.title if hasattr(self.bdf.case_control_deck, 'title') else "",
-            "units": {
-                "length": "mm",  # You may need to specify this
-                "force": "N",
-                "stress": "Pa",
-                "temperature": "K"
-            }
-        }
-    
-    def extract_mesh(self):
-        """
-        Extract mesh data from BDF
-        """
-        print("Extracting mesh...")
-        
-        # Nodes
-        nodes = []
-        node_ids = []
-        for nid, node in sorted(self.bdf.nodes.items()):
-            node_ids.append(nid)
-            nodes.append(node.get_position())
-        
-        nodes_array = np.array(nodes)
-        
-        # Elements
-        element_data = {
-            "solid": [],
-            "shell": [],
-            "beam": [],
-            "rigid": []
-        }
-        
-        # Solid elements (TETRA, HEXA, PENTA)
-        for eid, elem in self.bdf.elements.items():
-            elem_type = elem.type
-            
-            if elem_type in ['CTETRA', 'CHEXA', 'CPENTA', 'CTETRA10', 'CHEXA20']:
-                element_data["solid"].append({
-                    "id": eid,
-                    "type": elem_type,
-                    "nodes": elem.node_ids,
-                    "material_id": elem.mid,
-                    "property_id": elem.pid if hasattr(elem, 'pid') else None
-                })
-                
-            elif elem_type in ['CQUAD4', 'CTRIA3', 'CQUAD8', 'CTRIA6']:
-                element_data["shell"].append({
-                    "id": eid,
-                    "type": elem_type,
-                    "nodes": elem.node_ids,
-                    "material_id": elem.mid,
-                    "property_id": elem.pid,
-                    "thickness": elem.T() if hasattr(elem, 'T') else None
-                })
-                
-            elif elem_type in ['CBAR', 'CBEAM', 'CROD']:
-                element_data["beam"].append({
-                    "id": eid,
-                    "type": elem_type,
-                    "nodes": elem.node_ids,
-                    "material_id": elem.mid,
-                    "property_id": elem.pid
-                })
-                
-            elif elem_type in ['RBE2', 'RBE3', 'RBAR']:
-                element_data["rigid"].append({
-                    "id": eid,
-                    "type": elem_type,
-                    "nodes": elem.node_ids
-                })
-        
-        # Store mesh data
-        self.neural_field_data["mesh"] = {
-            "statistics": {
-                "n_nodes": len(nodes),
-                "n_elements": len(self.bdf.elements),
-                "element_types": {
-                    "solid": len(element_data["solid"]),
-                    "shell": len(element_data["shell"]),
-                    "beam": len(element_data["beam"]),
-                    "rigid": len(element_data["rigid"])
-                }
-            },
-            "nodes": {
-                "ids": node_ids,
-                "coordinates": nodes_array.tolist(),
-                "shape": list(nodes_array.shape)
-            },
-            "elements": element_data
-        }
-    
-    def extract_materials(self):
-        """
-        Extract material properties
-        """
-        print("Extracting materials...")
-        
-        materials = []
-        for mid, mat in self.bdf.materials.items():
-            mat_data = {
-                "id": mid,
-                "type": mat.type
-            }
-            
-            if mat.type == 'MAT1':  # Isotropic material
-                mat_data.update({
-                    "E": mat.e,      # Young's modulus
-                    "nu": mat.nu,    # Poisson's ratio
-                    "rho": mat.rho,  # Density
-                    "G": mat.g,      # Shear modulus
-                    "alpha": mat.a if hasattr(mat, 'a') else None,  # Thermal expansion
-                    "tref": mat.tref if hasattr(mat, 'tref') else None,
-                    "ST": mat.St() if hasattr(mat, 'St') else None,  # Tensile stress limit
-                    "SC": mat.Sc() if hasattr(mat, 'Sc') else None,  # Compressive stress limit
-                    "SS": mat.Ss() if hasattr(mat, 'Ss') else None   # Shear stress limit
-                })
-                
-            materials.append(mat_data)
-        
-        self.neural_field_data["materials"] = materials
-    
-    def extract_boundary_conditions(self):
-        """
-        Extract boundary conditions from BDF
-        """
-        print("Extracting boundary conditions...")
-        
-        bcs = {
-            "spc": [],      # Single point constraints
-            "mpc": [],      # Multi-point constraints
-            "suport": []    # Free body supports
-        }
-        
-        # SPC (fixed DOFs)
-        for spc_id, spc_list in self.bdf.spcs.items():
-            for spc in spc_list:
-                bcs["spc"].append({
-                    "id": spc_id,
-                    "node": spc.node_ids[0] if hasattr(spc, 'node_ids') else spc.node,
-                    "dofs": spc.components,  # Which DOFs are constrained (123456)
-                    "enforced_motion": spc.enforced
-                })
-        
-        # MPC equations
-        for mpc_id, mpc_list in self.bdf.mpcs.items():
-            for mpc in mpc_list:
-                bcs["mpc"].append({
-                    "id": mpc_id,
-                    "nodes": mpc.node_ids,
-                    "coefficients": mpc.coefficients,
-                    "components": mpc.components
-                })
-        
-        self.neural_field_data["boundary_conditions"] = bcs
-    
-    def extract_loads(self):
-        """
-        Extract loads from BDF
-        """
-        print("Extracting loads...")
-        
-        loads = {
-            "point_forces": [],
-            "pressure": [],
-            "gravity": [],
-            "thermal": []
-        }
-        
-        # Point forces (FORCE, MOMENT)
-        for load_id, load_list in self.bdf.loads.items():
-            for load in load_list:
-                if load.type == 'FORCE':
-                    loads["point_forces"].append({
-                        "id": load_id,
-                        "node": load.node,
-                        "magnitude": load.mag,
-                        "direction": [load.xyz[0], load.xyz[1], load.xyz[2]],
-                        "coord_system": load.cid
-                    })
-                    
-                elif load.type == 'MOMENT':
-                    loads["point_forces"].append({
-                        "id": load_id,
-                        "node": load.node,
-                        "moment": load.mag,
-                        "direction": [load.xyz[0], load.xyz[1], load.xyz[2]],
-                        "coord_system": load.cid
-                    })
-                    
-                elif load.type in ['PLOAD', 'PLOAD2', 'PLOAD4']:
-                    loads["pressure"].append({
-                        "id": load_id,
-                        "elements": load.element_ids,
-                        "pressure": load.pressure,
-                        "type": load.type
-                    })
-                    
-                elif load.type == 'GRAV':
-                    loads["gravity"].append({
-                        "id": load_id,
-                        "acceleration": load.scale,
-                        "direction": [load.N[0], load.N[1], load.N[2]],
-                        "coord_system": load.cid
-                    })
-        
-        # Temperature loads
-        for temp_id, temp_list in self.bdf.temps.items():
-            for temp in temp_list:
-                loads["thermal"].append({
-                    "id": temp_id,
-                    "node": temp.node,
-                    "temperature": temp.temperature
-                })
-        
-        self.neural_field_data["loads"] = loads
-    
-    def extract_results(self):
-        """
-        Extract results from OP2
-        """
-        print("Extracting results...")
-        
-        results = {}
-        
-        # Get subcase ID (usually 1 for linear static)
-        subcase_id = 1
-        
-        # Displacement
-        if hasattr(self.op2, 'displacements'):
-            disp = self.op2.displacements[subcase_id]
-            disp_data = disp.data[0, :, :]  # [itime=0, all_nodes, 6_dofs]
-            
-            results["displacement"] = {
-                "node_ids": disp.node_gridtype[:, 0].tolist(),
-                "data": disp_data.tolist(),
-                "shape": list(disp_data.shape),
-                "max_magnitude": float(np.max(np.linalg.norm(disp_data[:, :3], axis=1)))
-            }
-        
-        # Stress - handle different element types
-        stress_results = {}
-        
-        # Solid stress
-        if hasattr(self.op2, 'ctetra_stress'):
-            stress = self.op2.ctetra_stress[subcase_id]
-            stress_data = stress.data[0, :, :]
-            stress_results["solid_stress"] = {
-                "element_ids": stress.element_node[:, 0].tolist(),
-                "data": stress_data.tolist(),
-                "von_mises": stress_data[:, -1].tolist() if stress_data.shape[1] > 6 else None
-            }
-        
-        # Shell stress  
-        if hasattr(self.op2, 'cquad4_stress'):
-            stress = self.op2.cquad4_stress[subcase_id]
-            stress_data = stress.data[0, :, :]
-            stress_results["shell_stress"] = {
-                "element_ids": stress.element_node[:, 0].tolist(),
-                "data": stress_data.tolist()
-            }
-        
-        results["stress"] = stress_results
-        
-        # Strain
-        strain_results = {}
-        if hasattr(self.op2, 'ctetra_strain'):
-            strain = self.op2.ctetra_strain[subcase_id]
-            strain_data = strain.data[0, :, :]
-            strain_results["solid_strain"] = {
-                "element_ids": strain.element_node[:, 0].tolist(),
-                "data": strain_data.tolist()
-            }
-        
-        results["strain"] = strain_results
-        
-        # SPC Forces (reactions)
-        if hasattr(self.op2, 'spc_forces'):
-            spc = self.op2.spc_forces[subcase_id]
-            spc_data = spc.data[0, :, :]
-            results["reactions"] = {
-                "node_ids": spc.node_gridtype[:, 0].tolist(),
-                "forces": spc_data.tolist()
-            }
-        
-        self.neural_field_data["results"] = results
-    
-    def save_data(self):
-        """
-        Save parsed data to JSON and HDF5
-        """
-        print("Saving data...")
-        
-        # Save JSON metadata
-        json_file = self.case_dir / "neural_field_data.json"
-        with open(json_file, 'w') as f:
-            # Convert numpy arrays to lists for JSON serialization
-            json.dump(self.neural_field_data, f, indent=2, default=str)
-        
-        # Save HDF5 for large arrays
-        h5_file = self.case_dir / "neural_field_data.h5"
-        with h5py.File(h5_file, 'w') as f:
-            # Save mesh data
-            mesh_grp = f.create_group('mesh')
-            mesh_grp.create_dataset('node_coordinates', 
-                                   data=np.array(self.neural_field_data["mesh"]["nodes"]["coordinates"]))
-            
-            # Save results
-            if "results" in self.neural_field_data:
-                results_grp = f.create_group('results')
-                if "displacement" in self.neural_field_data["results"]:
-                    results_grp.create_dataset('displacement', 
-                                             data=np.array(self.neural_field_data["results"]["displacement"]["data"]))
-        
-        print(f"Data saved to {json_file} and {h5_file}")
-
-# ============================================================================
-# USAGE SCRIPT
-# ============================================================================
-
-def main():
-    """
-    Main function to run the parser
-    """
-    import sys
-    
-    if len(sys.argv) < 2:
-        print("Usage: python neural_field_parser.py <case_directory>")
-        sys.exit(1)
-    
-    case_dir = sys.argv[1]
-    
-    # Create parser
-    parser = NastranToNeuralFieldParser(case_dir)
-    
-    # Parse all data
-    try:
-        data = parser.parse_all()
-        print("\nParsing successful!")
-        print(f"Nodes: {data['mesh']['statistics']['n_nodes']}")
-        print(f"Elements: {data['mesh']['statistics']['n_elements']}")
-        print(f"Materials: {len(data['materials'])}")
-        
-    except Exception as e:
-        print(f"\nError during parsing: {e}")
-        import traceback
-        traceback.print_exc()
-
-if __name__ == "__main__":
-    main()
-
-Validation Script
-python"""
-validate_parsed_data.py
-Validates the parsed neural field data
-"""
-
-import json
-import h5py
-import numpy as np
-from pathlib import Path
-
-class NeuralFieldDataValidator:
-    """
-    Validates parsed data for completeness and consistency
-    """
-    
-    def __init__(self, case_directory):
-        self.case_dir = Path(case_directory)
-        self.json_file = self.case_dir / "neural_field_data.json"
-        self.h5_file = self.case_dir / "neural_field_data.h5"
-        
-    def validate(self):
-        """
-        Run all validation checks
-        """
-        print("Starting validation...")
-        
-        # Load data
-        with open(self.json_file, 'r') as f:
-            data = json.load(f)
-        
-        # Check required fields
-        required_fields = [
-            "metadata", "mesh", "materials", 
-            "boundary_conditions", "loads", "results"
-        ]
-        
-        for field in required_fields:
-            if field not in data:
-                print(f"❌ Missing required field: {field}")
-                return False
-            else:
-                print(f"✅ Found {field}")
-        
-        # Validate mesh
-        n_nodes = data["mesh"]["statistics"]["n_nodes"]
-        n_elements = data["mesh"]["statistics"]["n_elements"]
-        
-        print(f"\nMesh Statistics:")
-        print(f"  Nodes: {n_nodes}")
-        print(f"  Elements: {n_elements}")
-        
-        # Check results consistency
-        if "displacement" in data["results"]:
-            disp_nodes = len(data["results"]["displacement"]["node_ids"])
-            if disp_nodes != n_nodes:
-                print(f"⚠️  Displacement nodes ({disp_nodes}) != mesh nodes ({n_nodes})")
-        
-        # Check HDF5 file
-        with h5py.File(self.h5_file, 'r') as f:
-            print(f"\nHDF5 Contents:")
-            for key in f.keys():
-                print(f"  {key}: {list(f[key].keys())}")
-        
-        print("\n✅ Validation complete!")
-        return True
-
-if __name__ == "__main__":
-    import sys
-    validator = NeuralFieldDataValidator(sys.argv[1])
-    validator.validate()
-
-Step-by-Step Usage Instructions
-1. Prepare Your Analysis
-bash# In NX:
-1. Create geometry
-2. Generate mesh
-3. Apply materials (MAT1 cards)
-4. Apply constraints (SPC)
-5. Apply loads (FORCE, PLOAD4)
-6. Run SOL 101 (Linear Static)
-7. Request output: DISPLACEMENT=ALL, STRESS=ALL, STRAIN=ALL
-2. Organize Files
-bashmkdir training_case_001
-mkdir training_case_001/input
-mkdir training_case_001/output
-
-# Copy files
-cp your_model.bdf training_case_001/input/model.bdf
-cp your_model.op2 training_case_001/output/model.op2
-cp your_model.f06 training_case_001/output/model.f06
-3. Run Parser
-bash# Install requirements
-pip install pyNastran numpy h5py
-
-# Run parser
-python neural_field_parser.py training_case_001
-
-# Validate
-python validate_parsed_data.py training_case_001
-4. Check Output
-You'll get:
-
-neural_field_data.json - Complete metadata and structure
-neural_field_data.h5 - Large arrays (mesh, results)
-
-
-Automation Script for Multiple Cases
-python"""
-batch_parser.py
-Parse multiple cases automatically
-"""
-
-import os
-from pathlib import Path
-from neural_field_parser import NastranToNeuralFieldParser
-
-def batch_parse(root_directory):
-    """
-    Parse all cases in directory
-    """
-    root = Path(root_directory)
-    cases = [d for d in root.iterdir() if d.is_dir()]
-    
-    results = []
-    for case in cases:
-        print(f"\nProcessing {case.name}...")
-        try:
-            parser = NastranToNeuralFieldParser(case)
-            data = parser.parse_all()
-            results.append({
-                "case": case.name,
-                "status": "success",
-                "nodes": data["mesh"]["statistics"]["n_nodes"],
-                "elements": data["mesh"]["statistics"]["n_elements"]
-            })
-        except Exception as e:
-            results.append({
-                "case": case.name,
-                "status": "failed",
-                "error": str(e)
-            })
-    
-    # Summary
-    print("\n" + "="*50)
-    print("BATCH PROCESSING COMPLETE")
-    print("="*50)
-    for r in results:
-        status = "✅" if r["status"] == "success" else "❌"
-        print(f"{status} {r['case']}: {r['status']}")
-    
-    return results
-
-if __name__ == "__main__":
-    batch_parse("./training_data")
-
-What to Add Manually
-Create a metadata.json in each case directory with design intent:
-json{
-  "design_parameters": {
-    "thickness": 2.5,
-    "fillet_radius": 5.0,
-    "rib_height": 15.0
-  },
-  "optimization_context": {
-    "objectives": ["minimize_weight", "minimize_stress"],
-    "constraints": ["max_displacement < 2mm"],
-    "iteration": 42
-  },
-  "notes": "Baseline design with standard loading"
-}
-
-Troubleshooting
-Common Issues:
-
-"Can't find BDF nodes"
-
-Make sure you're using .bdf or .dat, not .sim
-Check that mesh was exported to solver deck
-
-
-"OP2 has no results"
-
-Ensure analysis completed successfully
-Check that you requested output (DISP=ALL, STRESS=ALL)
-
-
-"Memory error with large models"
-
-Use HDF5 chunking for very large models
-Process in batches
-
-
-
-This parser gives you everything you need to start training neural networks on your FEA data. The format is future-proof and will work with your automated generation pipeline!

atomizer-field/PHASE2_README.md

@@ -1,587 +0,0 @@
-
-
-# AtomizerField Phase 2: Neural Field Learning
-
-**Version 2.0.0**
-
-Phase 2 implements Graph Neural Networks (GNNs) to learn complete FEA field results from mesh geometry, boundary conditions, and loads. This enables 1000x faster structural analysis for optimization.
-
-## What's New in Phase 2
-
-### The Revolutionary Approach
-
-**Traditional FEA Surrogate Models:**
-```
-Parameters → Neural Network → Max Stress (scalar)
-```
-- Only learns maximum values
-- Loses all spatial information
-- Can't understand physics
-- Limited to specific loading conditions
-
-**AtomizerField Neural Field Learning:**
-```
-Mesh + BCs + Loads → Graph Neural Network → Complete Stress Field (45,000 values)
-```
-- Learns complete field distributions
-- Understands how forces flow through structure
-- Physics-informed constraints
-- Generalizes to new loading conditions
-
-### Key Components
-
-1. **Graph Neural Network Architecture** - Learns on mesh topology
-2. **Physics-Informed Loss Functions** - Enforces physical laws
-3. **Efficient Data Loading** - Handles large FEA datasets
-4. **Training Pipeline** - Multi-GPU support, checkpointing, early stopping
-5. **Fast Inference** - Millisecond predictions vs hours of FEA
-
-## Quick Start
-
-### 1. Installation
-
-```bash
-# Install Phase 2 dependencies (PyTorch, PyTorch Geometric)
-pip install -r requirements.txt
-```
-
-### 2. Prepare Training Data
-
-First, you need parsed FEA data from Phase 1:
-
-```bash
-# Parse your NX Nastran results (from Phase 1)
-python neural_field_parser.py training_case_001
-python neural_field_parser.py training_case_002
-# ... repeat for all training cases
-
-# Organize into train/val splits
-mkdir training_data
-mkdir validation_data
-
-# Move 80% of cases to training_data/
-# Move 20% of cases to validation_data/
-```
-
-### 3. Train Model
-
-```bash
-# Basic training
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 100 \
-    --batch_size 4 \
-    --lr 0.001
-
-# Advanced training with physics-informed loss
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 200 \
-    --batch_size 8 \
-    --lr 0.001 \
-    --loss_type physics \
-    --hidden_dim 256 \
-    --num_layers 8 \
-    --output_dir ./my_model
-```
-
-### 4. Run Inference
-
-```bash
-# Predict on single case
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input test_case_001 \
-    --compare
-
-# Batch prediction on multiple cases
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input ./test_data \
-    --batch \
-    --output_dir ./predictions
-```
-
-## Architecture Deep Dive
-
-### Graph Neural Network (GNN)
-
-Our GNN architecture respects the physics of structural mechanics:
-
-```
-Input Graph:
-- Nodes: FEA mesh nodes
-  * Position (x, y, z)
-  * Boundary conditions (6 DOF constraints)
-  * Applied loads (force vectors)
-
-- Edges: Element connectivity
-  * Material properties (E, ν, ρ, G, α)
-  * Element type (solid, shell, beam)
-
-Message Passing (6 layers):
-Each layer propagates information through mesh:
-1. Gather information from neighbors
-2. Update node representations
-3. Respect mesh topology (forces flow through connected elements)
-
-Output:
-- Displacement field: [num_nodes, 6] (3 translation + 3 rotation)
-- Stress field: [num_nodes, 6] (σxx, σyy, σzz, τxy, τyz, τxz)
-- Von Mises stress: [num_nodes, 1]
-```
-
-### Why This Works
-
-**Key Insight**: FEA solves:
-```
-K u = f
-```
-Where K depends on mesh topology and materials.
-
-Our GNN learns this relationship:
-- **Mesh topology** → Graph edges
-- **Material properties** → Edge features
-- **Boundary conditions** → Node features
-- **Loads** → Node features
-- **Message passing** → Learns stiffness matrix behavior
-
-Result: The network learns physics, not just patterns!
-
-### Model Architecture Details
-
-```python
-AtomizerFieldModel:
-  ├── Node Encoder (12 → 128 dim)
-  │   └── Coordinates (3) + BCs (6) + Loads (3)
-  │
-  ├── Edge Encoder (5 → 64 dim)
-  │   └── Material properties (E, ν, ρ, G, α)
-  │
-  ├── Message Passing Layers (6 layers)
-  │   ├── MeshGraphConv
-  │   ├── Layer Normalization
-  │   ├── Residual Connection
-  │   └── Dropout
-  │
-  ├── Displacement Decoder (128 → 6)
-  │   └── Outputs: u_x, u_y, u_z, θ_x, θ_y, θ_z
-  │
-  └── Stress Predictor (6 → 6)
-      └── Outputs: σxx, σyy, σzz, τxy, τyz, τxz
-
-Total Parameters: ~718,000
-```
-
-## Physics-Informed Loss Functions
-
-Standard neural networks only minimize prediction error. We also enforce physics:
-
-### 1. Data Loss
-```
-L_data = ||u_pred - u_FEA||² + ||σ_pred - σ_FEA||²
-```
-Ensures predictions match FEA ground truth.
-
-### 2. Equilibrium Loss
-```
-L_eq = ||∇·σ + f||²
-```
-Forces must balance at every point.
-
-### 3. Constitutive Loss
-```
-L_const = ||σ - C:ε||²
-```
-Stress must follow material law (σ = C:ε).
-
-### 4. Boundary Condition Loss
-```
-L_bc = ||u||² at fixed nodes
-```
-Displacement must be zero at constraints.
-
-### Total Loss
-```
-L_total = λ_data·L_data + λ_eq·L_eq + λ_const·L_const + λ_bc·L_bc
-```
-
-**Benefits:**
-- Faster convergence
-- Better generalization
-- Physically plausible predictions
-- Works with less training data
-
-## Training Guide
-
-### Dataset Requirements
-
-**Minimum Dataset Size:**
-- Small models (< 10k elements): 50-100 cases
-- Medium models (10k-100k elements): 100-500 cases
-- Large models (> 100k elements): 500-1000 cases
-
-**Data Diversity:**
-Vary these parameters across training cases:
-- Geometry (thicknesses, radii, dimensions)
-- Loading conditions (magnitude, direction, location)
-- Boundary conditions (support locations, constrained DOFs)
-- Materials (within reason - same element types)
-
-### Training Best Practices
-
-**1. Start Simple:**
-```bash
-# First, train with MSE loss only
-python train.py --loss_type mse --epochs 50
-```
-
-**2. Add Physics:**
-```bash
-# Then add physics-informed constraints
-python train.py --loss_type physics --epochs 100
-```
-
-**3. Tune Hyperparameters:**
-```bash
-# Increase model capacity for complex geometries
-python train.py \
-    --hidden_dim 256 \
-    --num_layers 8 \
-    --dropout 0.15
-```
-
-**4. Monitor Training:**
-```bash
-# View training progress in TensorBoard
-tensorboard --logdir runs/tensorboard
-```
-
-### Typical Training Time
-
-On a single GPU (e.g., NVIDIA RTX 3080):
-- Small dataset (100 cases, 10k elements each): 2-4 hours
-- Medium dataset (500 cases, 50k elements each): 8-12 hours
-- Large dataset (1000 cases, 100k elements each): 24-48 hours
-
-**Speedup Tips:**
-- Use multiple GPUs: `CUDA_VISIBLE_DEVICES=0,1 python train.py`
-- Increase batch size if memory allows
-- Use mixed precision training (future feature)
-- Cache data in RAM: set `cache_in_memory=True` in data_loader.py
-
-## Inference Performance
-
-### Speed Comparison
-
-| Analysis Method | Time | Speedup |
-|----------------|------|---------|
-| Traditional FEA (NX Nastran) | 2-3 hours | 1x |
-| **AtomizerField GNN** | **5-50 ms** | **10,000x** |
-
-**Real Performance (100k element model):**
-- FEA Setup + Solve: ~2 hours
-- Neural Network Inference: ~15 milliseconds
-- **Speedup: 480,000x**
-
-This enables:
-- Interactive design exploration
-- Real-time optimization (evaluate millions of designs)
-- Instant "what-if" analysis
-
-### Accuracy
-
-Typical prediction errors (on validation set):
-- **Displacement**: 2-5% relative error
-- **Stress**: 5-10% relative error
-- **Max values**: 1-3% relative error
-
-**When It Works Best:**
-- Interpolation (designs within training range)
-- Similar loading conditions
-- Same support configurations
-- Parametric variations
-
-**When to Use with Caution:**
-- Extreme extrapolation (far outside training data)
-- Completely new loading scenarios
-- Different element types than training
-- Nonlinear materials (future work)
-
-## Usage Examples
-
-### Example 1: Rapid Design Optimization
-
-```python
-from predict import FieldPredictor
-
-# Load trained model
-predictor = FieldPredictor('checkpoint_best.pt')
-
-# Test 1000 design variants
-results = []
-for design_params in design_space:
-    # Generate FEA input (don't solve!)
-    create_nastran_model(design_params)
-    parse_to_neural_format(design_params)
-
-    # Predict in milliseconds
-    pred = predictor.predict(f'design_{i}')
-
-    results.append({
-        'params': design_params,
-        'max_stress': pred['max_stress'],
-        'max_displacement': pred['max_displacement']
-    })
-
-# Find optimal design
-best = min(results, key=lambda r: r['max_stress'])
-print(f"Best design: {best['params']}")
-print(f"Stress: {best['max_stress']:.2f} MPa")
-```
-
-**Result:** Evaluate 1000 designs in ~30 seconds instead of 3000 hours!
-
-### Example 2: Interactive Design Tool
-
-```python
-# Real-time design feedback
-while user_editing:
-    # User modifies geometry
-    updated_geometry = get_user_input()
-
-    # Generate mesh (fast, no solve)
-    mesh = generate_mesh(updated_geometry)
-    parse_mesh(mesh)
-
-    # Instant prediction
-    prediction = predictor.predict('current_design')
-
-    # Show results immediately
-    display_stress_field(prediction['von_mises'])
-    display_displacement(prediction['displacement'])
-
-    # Immediate feedback: "Max stress: 450 MPa (SAFE)"
-```
-
-**Result:** Engineer sees results instantly, not hours later!
-
-### Example 3: Optimization with Physics Understanding
-
-```python
-# Traditional: Only knows max_stress = 450 MPa
-# AtomizerField: Knows WHERE stress concentrations are!
-
-prediction = predictor.predict('current_design')
-stress_field = prediction['von_mises']
-
-# Find stress hotspots
-hotspots = find_nodes_above_threshold(stress_field, threshold=400)
-
-# Intelligent design suggestions
-for hotspot in hotspots:
-    location = mesh.nodes[hotspot].position
-    suggest_reinforcement(location)  # Add material where needed
-    suggest_fillet(location)  # Smooth sharp corners
-```
-
-**Result:** Optimization guided by physics, not blind search!
-
-## File Structure
-
-```
-Atomizer-Field/
-├── neural_models/
-│   ├── __init__.py
-│   ├── field_predictor.py       # GNN architecture
-│   ├── physics_losses.py        # Loss functions
-│   └── data_loader.py           # Data pipeline
-│
-├── train.py                     # Training script
-├── predict.py                   # Inference script
-├── requirements.txt             # Dependencies
-│
-├── runs/                        # Training outputs
-│   ├── checkpoint_best.pt       # Best model
-│   ├── checkpoint_latest.pt     # Latest checkpoint
-│   ├── tensorboard/             # Training logs
-│   └── config.json              # Model configuration
-│
-└── training_data/               # Parsed FEA cases
-    ├── case_001/
-    │   ├── neural_field_data.json
-    │   └── neural_field_data.h5
-    ├── case_002/
-    └── ...
-```
-
-## Troubleshooting
-
-### Training Issues
-
-**Problem: Loss not decreasing**
-```bash
-# Solutions:
-# 1. Lower learning rate
-python train.py --lr 0.0001
-
-# 2. Check data normalization
-# Ensure normalize=True in data_loader.py
-
-# 3. Start with simpler loss
-python train.py --loss_type mse
-```
-
-**Problem: Out of memory**
-```bash
-# Solutions:
-# 1. Reduce batch size
-python train.py --batch_size 2
-
-# 2. Reduce model size
-python train.py --hidden_dim 64 --num_layers 4
-
-# 3. Use gradient accumulation (future feature)
-```
-
-**Problem: Overfitting**
-```bash
-# Solutions:
-# 1. Increase dropout
-python train.py --dropout 0.2
-
-# 2. Get more training data
-# 3. Use data augmentation (rotate/scale meshes)
-```
-
-### Inference Issues
-
-**Problem: Poor predictions**
-- Check if test case is within training distribution
-- Verify data normalization matches training
-- Ensure model finished training (check validation loss)
-
-**Problem: Slow inference**
-- Use GPU: `--device cuda`
-- Batch multiple predictions together
-- Use smaller model for production
-
-## Advanced Topics
-
-### Transfer Learning
-
-Train on one component type, fine-tune on another:
-
-```bash
-# 1. Train base model on brackets
-python train.py --train_dir brackets/ --epochs 100
-
-# 2. Fine-tune on beams (similar physics, different geometry)
-python train.py \
-    --train_dir beams/ \
-    --resume runs/checkpoint_best.pt \
-    --epochs 50 \
-    --lr 0.0001  # Lower LR for fine-tuning
-```
-
-### Multi-Fidelity Learning
-
-Combine coarse and fine meshes:
-
-```python
-# Train on mix of mesh resolutions
-train_cases = [
-    *coarse_mesh_cases,   # Fast to solve, less accurate
-    *fine_mesh_cases      # Slow to solve, very accurate
-]
-
-# Model learns to predict fine-mesh accuracy at coarse-mesh speed!
-```
-
-### Physics-Based Data Augmentation
-
-```python
-# Augment training data with physical transformations
-def augment_case(mesh, displacement, stress):
-    # Rotate entire structure
-    mesh_rotated = rotate(mesh, angle=random.uniform(0, 360))
-    displacement_rotated = rotate_vector_field(displacement, angle)
-    stress_rotated = rotate_tensor_field(stress, angle)
-
-    # Scale loads (linear scaling)
-    scale = random.uniform(0.5, 2.0)
-    displacement_scaled = displacement * scale
-    stress_scaled = stress * scale
-
-    return augmented_cases
-```
-
-## Future Enhancements (Phase 3)
-
-- [ ] Nonlinear analysis support (plasticity, large deformation)
-- [ ] Contact and friction
-- [ ] Composite materials
-- [ ] Modal analysis (natural frequencies)
-- [ ] Thermal coupling
-- [ ] Topology optimization integration
-- [ ] Atomizer dashboard integration
-- [ ] Cloud deployment for team access
-
-## Performance Benchmarks
-
-### Model Accuracy (Validation Set)
-
-| Metric | Error | Target |
-|--------|-------|--------|
-| Displacement MAE | 0.003 mm | < 0.01 mm |
-| Displacement Relative Error | 3.2% | < 5% |
-| Stress MAE | 12.5 MPa | < 20 MPa |
-| Max Stress Error | 2.1% | < 5% |
-| Max Displacement Error | 1.8% | < 3% |
-
-### Computational Performance
-
-| Dataset | FEA Time | NN Time | Speedup |
-|---------|----------|---------|---------|
-| 10k elements | 15 min | 5 ms | 180,000x |
-| 50k elements | 2 hours | 15 ms | 480,000x |
-| 100k elements | 8 hours | 35 ms | 823,000x |
-
-**Hardware:** Single NVIDIA RTX 3080, Intel i9-12900K
-
-## Citation
-
-If you use AtomizerField in research, please cite:
-
-```
-@software{atomizerfield2024,
-  title={AtomizerField: Neural Field Learning for Structural Optimization},
-  author={Your Name},
-  year={2024},
-  url={https://github.com/yourusername/atomizer-field}
-}
-```
-
-## Support
-
-For issues or questions about Phase 2:
-
-1. Check this README and PHASE2_README.md
-2. Review training logs in TensorBoard
-3. Examine model predictions vs ground truth
-4. Check GPU memory usage and batch size
-5. Verify data normalization
-
-## What's Next?
-
-- **Phase 3**: Integration with main Atomizer platform
-- **Phase 4**: Production deployment and dashboard
-- **Phase 5**: Multi-user cloud platform
-
----
-
-**AtomizerField Phase 2**: Revolutionary neural field learning for structural optimization.
-
-*1000x faster than FEA. Physics-informed. Production-ready.*

atomizer-field/QUICK_REFERENCE.md

@@ -1,500 +0,0 @@
-# AtomizerField Quick Reference Guide
-
-**Version 1.0** | Complete Implementation | Ready for Training
-
----
-
-## 🎯 What is AtomizerField?
-
-Neural field learning system that replaces FEA with 1000× faster graph neural networks.
-
-**Key Innovation:** Learn complete stress/displacement FIELDS (45,000+ values), not just max values.
-
----
-
-## 📁 Project Structure
-
-```
-Atomizer-Field/
-├── Neural Network Core
-│   ├── neural_models/
-│   │   ├── field_predictor.py      # GNN architecture (718K params)
-│   │   ├── physics_losses.py       # 4 loss functions
-│   │   ├── data_loader.py          # PyTorch Geometric dataset
-│   │   └── uncertainty.py          # Ensemble + online learning
-│   ├── train.py                    # Training pipeline
-│   ├── predict.py                  # Inference engine
-│   └── optimization_interface.py   # Atomizer integration
-│
-├── Data Pipeline
-│   ├── neural_field_parser.py      # BDF/OP2 → neural format
-│   ├── validate_parsed_data.py     # Data quality checks
-│   └── batch_parser.py             # Multi-case processing
-│
-├── Testing (18 tests)
-│   ├── test_suite.py               # Master orchestrator
-│   ├── test_simple_beam.py         # Simple Beam validation
-│   └── tests/
-│       ├── test_synthetic.py       # 5 smoke tests
-│       ├── test_physics.py         # 4 physics tests
-│       ├── test_learning.py        # 4 learning tests
-│       ├── test_predictions.py     # 5 integration tests
-│       └── analytical_cases.py     # Analytical solutions
-│
-└── Documentation (10 guides)
-    ├── README.md                   # Project overview
-    ├── IMPLEMENTATION_STATUS.md    # Complete status
-    ├── TESTING_COMPLETE.md         # Testing guide
-    └── ... (7 more guides)
-```
-
----
-
-## 🚀 Quick Start Commands
-
-### 1. Test the System
-```bash
-# Smoke tests (30 seconds) - Once environment fixed
-python test_suite.py --quick
-
-# Test with Simple Beam
-python test_simple_beam.py
-
-# Full test suite (1 hour)
-python test_suite.py --full
-```
-
-### 2. Parse FEA Data
-```bash
-# Single case
-python neural_field_parser.py path/to/case_directory
-
-# Validate parsed data
-python validate_parsed_data.py path/to/case_directory
-
-# Batch process multiple cases
-python batch_parser.py --input Models/ --output parsed_data/
-```
-
-### 3. Train Model
-```bash
-# Basic training
-python train.py --data_dirs case1 case2 case3 --epochs 100
-
-# With all options
-python train.py \
-  --data_dirs parsed_data/* \
-  --epochs 200 \
-  --batch_size 32 \
-  --lr 0.001 \
-  --loss physics \
-  --checkpoint_dir checkpoints/
-```
-
-### 4. Make Predictions
-```bash
-# Single prediction
-python predict.py --model checkpoints/best_model.pt --data test_case/
-
-# Batch prediction
-python predict.py --model best_model.pt --data test_cases/*.h5 --batch_size 64
-```
-
-### 5. Optimize with Atomizer
-```python
-from optimization_interface import NeuralFieldOptimizer
-
-# Initialize
-optimizer = NeuralFieldOptimizer('checkpoints/best_model.pt')
-
-# Evaluate design
-results = optimizer.evaluate(design_graph)
-print(f"Max stress: {results['max_stress']} MPa")
-print(f"Max displacement: {results['max_displacement']} mm")
-
-# Get gradients for optimization
-sensitivities = optimizer.get_sensitivities(design_graph)
-```
-
----
-
-## 📊 Key Metrics
-
-### Performance
-- **Training time:** 2-6 hours (50-500 cases, 100-200 epochs)
-- **Inference time:** 5-50ms (vs 30-300s FEA)
-- **Speedup:** 1000× faster than FEA
-- **Memory:** ~2GB GPU for training, ~500MB for inference
-
-### Accuracy (After Training)
-- **Target:** < 10% prediction error vs FEA
-- **Physics tests:** < 5% error on analytical solutions
-- **Learning tests:** < 5% interpolation error
-
-### Model Size
-- **Parameters:** 718,221
-- **Layers:** 6 message passing layers
-- **Input:** 12D node features, 5D edge features
-- **Output:** 6 DOF displacement + 6 stress components per node
-
----
-
-## 🧪 Testing Overview
-
-### Quick Smoke Test (30s)
-```bash
-python test_suite.py --quick
-```
-**5 tests:** Model creation, forward pass, losses, batch, gradients
-
-### Physics Validation (15 min)
-```bash
-python test_suite.py --physics
-```
-**9 tests:** Smoke + Cantilever, equilibrium, energy, constitutive
-
-### Learning Tests (30 min)
-```bash
-python test_suite.py --learning
-```
-**13 tests:** Smoke + Physics + Memorization, interpolation, extrapolation, patterns
-
-### Full Suite (1 hour)
-```bash
-python test_suite.py --full
-```
-**18 tests:** Complete validation from zero to production
-
----
-
-## 📈 Typical Workflow
-
-### Phase 1: Data Preparation
-```bash
-# 1. Parse FEA cases
-python batch_parser.py --input Models/ --output training_data/
-
-# 2. Validate data
-for dir in training_data/*; do
-  python validate_parsed_data.py $dir
-done
-
-# Expected: 50-500 parsed cases
-```
-
-### Phase 2: Training
-```bash
-# 3. Train model
-python train.py \
-  --data_dirs training_data/* \
-  --epochs 100 \
-  --batch_size 16 \
-  --loss physics \
-  --checkpoint_dir checkpoints/
-
-# Monitor with TensorBoard
-tensorboard --logdir runs/
-
-# Expected: Training loss < 0.01 after 100 epochs
-```
-
-### Phase 3: Validation
-```bash
-# 4. Run all tests
-python test_suite.py --full
-
-# 5. Test on new data
-python predict.py --model checkpoints/best_model.pt --data test_case/
-
-# Expected: All tests pass, < 10% error
-```
-
-### Phase 4: Deployment
-```python
-# 6. Integrate with Atomizer
-from optimization_interface import NeuralFieldOptimizer
-
-optimizer = NeuralFieldOptimizer('checkpoints/best_model.pt')
-
-# Use in optimization loop
-for iteration in range(100):
-    results = optimizer.evaluate(current_design)
-    sensitivities = optimizer.get_sensitivities(current_design)
-    # Update design based on gradients
-    current_design = update_design(current_design, sensitivities)
-```
-
----
-
-## 🔧 Configuration
-
-### Training Config (atomizer_field_config.yaml)
-```yaml
-model:
-  hidden_dim: 128
-  num_layers: 6
-  dropout: 0.1
-
-training:
-  batch_size: 16
-  learning_rate: 0.001
-  epochs: 100
-  early_stopping_patience: 10
-
-loss:
-  type: physics
-  lambda_data: 1.0
-  lambda_equilibrium: 0.1
-  lambda_constitutive: 0.1
-  lambda_boundary: 0.5
-
-uncertainty:
-  n_ensemble: 5
-  threshold: 0.1  # Trigger FEA if uncertainty > 10%
-
-online_learning:
-  enabled: true
-  update_frequency: 10  # Update every 10 FEA runs
-  batch_size: 32
-```
-
----
-
-## 🎓 Feature Reference
-
-### 1. Data Parser
-**File:** `neural_field_parser.py`
-
-```python
-from neural_field_parser import NastranToNeuralFieldParser
-
-# Parse case
-parser = NastranToNeuralFieldParser('case_directory')
-data = parser.parse_all()
-
-# Access results
-print(f"Nodes: {data['mesh']['statistics']['n_nodes']}")
-print(f"Max displacement: {data['results']['displacement']['max_translation']} mm")
-```
-
-### 2. Neural Model
-**File:** `neural_models/field_predictor.py`
-
-```python
-from neural_models.field_predictor import create_model
-
-# Create model
-config = {
-    'node_feature_dim': 12,
-    'edge_feature_dim': 5,
-    'hidden_dim': 128,
-    'num_layers': 6
-}
-model = create_model(config)
-
-# Predict
-predictions = model(graph_data, return_stress=True)
-# predictions['displacement']: (N, 6) - 6 DOF per node
-# predictions['stress']: (N, 6) - stress tensor
-# predictions['von_mises']: (N,) - von Mises stress
-```
-
-### 3. Physics Losses
-**File:** `neural_models/physics_losses.py`
-
-```python
-from neural_models.physics_losses import create_loss_function
-
-# Create loss
-loss_fn = create_loss_function('physics')
-
-# Compute loss
-losses = loss_fn(predictions, targets, data)
-# losses['total_loss']: Combined loss
-# losses['displacement_loss']: Data loss
-# losses['equilibrium_loss']: ∇·σ + f = 0
-# losses['constitutive_loss']: σ = C:ε
-# losses['boundary_loss']: BC compliance
-```
-
-### 4. Optimization Interface
-**File:** `optimization_interface.py`
-
-```python
-from optimization_interface import NeuralFieldOptimizer
-
-# Initialize
-optimizer = NeuralFieldOptimizer('model.pt')
-
-# Fast evaluation (15ms)
-results = optimizer.evaluate(graph_data)
-
-# Analytical gradients (1M× faster than FD)
-grads = optimizer.get_sensitivities(graph_data)
-```
-
-### 5. Uncertainty Quantification
-**File:** `neural_models/uncertainty.py`
-
-```python
-from neural_models.uncertainty import UncertainFieldPredictor
-
-# Create ensemble
-model = UncertainFieldPredictor(base_config, n_ensemble=5)
-
-# Predict with uncertainty
-predictions = model.predict_with_uncertainty(graph_data)
-# predictions['mean']: Mean prediction
-# predictions['std']: Standard deviation
-# predictions['confidence']: 95% confidence interval
-
-# Check if FEA needed
-if model.needs_fea_validation(predictions, threshold=0.1):
-    # Run FEA for this case
-    fea_result = run_fea(design)
-    # Update model online
-    model.update_online(graph_data, fea_result)
-```
-
----
-
-## 🐛 Troubleshooting
-
-### NumPy Environment Issue
-**Problem:** Segmentation fault when importing NumPy
-```
-CRASHES ARE TO BE EXPECTED - PLEASE REPORT THEM TO NUMPY DEVELOPERS
-Segmentation fault
-```
-
-**Solutions:**
-1. Use conda: `conda install numpy`
-2. Use WSL: Install Windows Subsystem for Linux
-3. Use Linux: Native Linux environment
-4. Reinstall: `pip uninstall numpy && pip install numpy`
-
-### Import Errors
-**Problem:** Cannot find modules
-```python
-ModuleNotFoundError: No module named 'torch_geometric'
-```
-
-**Solution:**
-```bash
-# Install all dependencies
-pip install -r requirements.txt
-
-# Or individual packages
-pip install torch torch-geometric pyg-lib
-pip install pyNastran h5py pyyaml tensorboard
-```
-
-### GPU Memory Issues
-**Problem:** CUDA out of memory during training
-
-**Solutions:**
-1. Reduce batch size: `--batch_size 8`
-2. Reduce model size: `hidden_dim: 64`
-3. Use CPU: `--device cpu`
-4. Enable gradient checkpointing
-
-### Poor Predictions
-**Problem:** High prediction error (> 20%)
-
-**Solutions:**
-1. Train longer: `--epochs 200`
-2. More data: Generate 200-500 training cases
-3. Use physics loss: `--loss physics`
-4. Check data quality: `python validate_parsed_data.py`
-5. Normalize data: `normalize=True` in dataset
-
----
-
-## 📚 Documentation Index
-
-1. **README.md** - Project overview and quick start
-2. **IMPLEMENTATION_STATUS.md** - Complete status report
-3. **TESTING_COMPLETE.md** - Comprehensive testing guide
-4. **PHASE2_README.md** - Neural network documentation
-5. **GETTING_STARTED.md** - Step-by-step tutorial
-6. **SYSTEM_ARCHITECTURE.md** - Technical architecture
-7. **ENHANCEMENTS_GUIDE.md** - Advanced features
-8. **FINAL_IMPLEMENTATION_REPORT.md** - Implementation details
-9. **TESTING_FRAMEWORK_SUMMARY.md** - Testing overview
-10. **QUICK_REFERENCE.md** - This guide
-
----
-
-## ⚡ Pro Tips
-
-### Training
-- Start with 50 cases to verify pipeline
-- Use physics loss for better generalization
-- Monitor TensorBoard for convergence
-- Save checkpoints every 10 epochs
-- Early stopping prevents overfitting
-
-### Data
-- Quality > Quantity: 50 good cases better than 200 poor ones
-- Diverse designs: Vary geometry, loads, materials
-- Validate data: Check for NaN, physics violations
-- Normalize features: Improves training stability
-
-### Performance
-- GPU recommended: 10× faster training
-- Batch size = GPU memory / model size
-- Use DataLoader workers: `num_workers=4`
-- Cache in memory: `cache_in_memory=True`
-
-### Uncertainty
-- Use ensemble (5 models) for confidence
-- Trigger FEA when uncertainty > 10%
-- Update online: Improves during optimization
-- Track confidence: Builds trust in predictions
-
----
-
-## 🎯 Success Checklist
-
-### Pre-Training
-- [x] All code implemented
-- [x] Tests written
-- [x] Documentation complete
-- [ ] Environment working (NumPy issue)
-
-### Training
-- [ ] 50-500 training cases generated
-- [ ] Data parsed and validated
-- [ ] Model trains without errors
-- [ ] Loss converges < 0.01
-
-### Validation
-- [ ] All tests pass
-- [ ] Physics compliance < 5% error
-- [ ] Prediction error < 10%
-- [ ] Inference < 50ms
-
-### Production
-- [ ] Integrated with Atomizer
-- [ ] 1000× speedup demonstrated
-- [ ] Uncertainty quantification working
-- [ ] Online learning enabled
-
----
-
-## 📞 Support
-
-**Current Status:** Implementation complete, ready for training
-
-**Next Steps:**
-1. Fix NumPy environment
-2. Generate training data
-3. Train and validate
-4. Deploy to production
-
-**All code is ready to use!** 🚀
-
----
-
-*AtomizerField Quick Reference v1.0*
-*~7,000 lines | 18 tests | 10 docs | Production Ready*

atomizer-field/README.md

@@ -1,548 +0,0 @@
-# AtomizerField Neural Field Data Parser
-
-**Version 1.0.0**
-
-A production-ready Python parser that converts NX Nastran FEA results into standardized neural field training data for the AtomizerField optimization platform.
-
-## What This Does
-
-Instead of extracting just scalar values (like maximum stress) from FEA results, this parser captures **complete field data** - stress, displacement, and strain at every node and element. This enables neural networks to learn the physics of how structures respond to loads, enabling 1000x faster optimization with true physics understanding.
-
-## Features
-
-- ✅ **Complete Field Extraction**: Captures displacement, stress, strain at ALL points
-- ✅ **Future-Proof Format**: Versioned data structure (v1.0) designed for years of neural network training
-- ✅ **Efficient Storage**: Uses HDF5 for large arrays, JSON for metadata
-- ✅ **Robust Parsing**: Handles mixed element types (solid, shell, beam, rigid)
-- ✅ **Data Validation**: Built-in physics and quality checks
-- ✅ **Batch Processing**: Process hundreds of cases automatically
-- ✅ **Production Ready**: Error handling, logging, provenance tracking
-
-## Quick Start
-
-### 1. Installation
-
-```bash
-# Install dependencies
-pip install -r requirements.txt
-```
-
-### 2. Prepare Your NX Nastran Analysis
-
-In NX:
-1. Create geometry and generate mesh
-2. Apply materials (MAT1 cards)
-3. Define boundary conditions (SPC)
-4. Apply loads (FORCE, PLOAD4, GRAV)
-5. Run **SOL 101** (Linear Static)
-6. Request output: `DISPLACEMENT=ALL`, `STRESS=ALL`, `STRAIN=ALL`
-
-### 3. Organize Files
-
-```bash
-mkdir training_case_001
-mkdir training_case_001/input
-mkdir training_case_001/output
-
-# Copy files
-cp your_model.bdf training_case_001/input/model.bdf
-cp your_model.op2 training_case_001/output/model.op2
-```
-
-### 4. Run Parser
-
-```bash
-# Parse single case
-python neural_field_parser.py training_case_001
-
-# Validate results
-python validate_parsed_data.py training_case_001
-```
-
-### 5. Check Output
-
-You'll get:
-- **neural_field_data.json** - Complete metadata and structure
-- **neural_field_data.h5** - Large arrays (mesh, field results)
-
-## Usage Guide
-
-### Single Case Parsing
-
-```bash
-python neural_field_parser.py <case_directory>
-```
-
-**Expected directory structure:**
-```
-training_case_001/
-├── input/
-│   ├── model.bdf          # Nastran input deck
-│   └── model.sim          # (optional) NX simulation file
-├── output/
-│   ├── model.op2          # Binary results (REQUIRED)
-│   └── model.f06          # (optional) ASCII results
-└── metadata.json          # (optional) Your design annotations
-```
-
-**Output:**
-```
-training_case_001/
-├── neural_field_data.json  # Metadata, structure, small arrays
-└── neural_field_data.h5    # Large arrays (coordinates, fields)
-```
-
-### Batch Processing
-
-Process multiple cases at once:
-
-```bash
-python batch_parser.py ./training_data
-```
-
-**Expected structure:**
-```
-training_data/
-├── case_001/
-│   ├── input/model.bdf
-│   └── output/model.op2
-├── case_002/
-│   ├── input/model.bdf
-│   └── output/model.op2
-└── case_003/
-    ├── input/model.bdf
-    └── output/model.op2
-```
-
-**Options:**
-```bash
-# Skip validation (faster)
-python batch_parser.py ./training_data --no-validate
-
-# Stop on first error
-python batch_parser.py ./training_data --stop-on-error
-```
-
-**Output:**
-- Parses all cases
-- Validates each one
-- Generates `batch_processing_summary.json` with results
-
-### Data Validation
-
-```bash
-python validate_parsed_data.py training_case_001
-```
-
-Checks:
-- ✓ File existence and format
-- ✓ Data completeness (all required fields)
-- ✓ Physics consistency (equilibrium, units)
-- ✓ Data quality (no NaN/inf, reasonable values)
-- ✓ Mesh integrity
-- ✓ Material property validity
-
-## Data Structure v1.0
-
-The parser produces a standardized data structure designed to be future-proof:
-
-```json
-{
-  "metadata": {
-    "version": "1.0.0",
-    "created_at": "timestamp",
-    "analysis_type": "SOL_101",
-    "units": {...}
-  },
-  "mesh": {
-    "statistics": {
-      "n_nodes": 15432,
-      "n_elements": 8765
-    },
-    "nodes": {
-      "ids": [...],
-      "coordinates": "stored in HDF5"
-    },
-    "elements": {
-      "solid": [...],
-      "shell": [...],
-      "beam": [...]
-    }
-  },
-  "materials": [...],
-  "boundary_conditions": {
-    "spc": [...],
-    "mpc": [...]
-  },
-  "loads": {
-    "point_forces": [...],
-    "pressure": [...],
-    "gravity": [...],
-    "thermal": [...]
-  },
-  "results": {
-    "displacement": "stored in HDF5",
-    "stress": "stored in HDF5",
-    "strain": "stored in HDF5",
-    "reactions": "stored in HDF5"
-  }
-}
-```
-
-### HDF5 Structure
-
-Large numerical arrays are stored in HDF5 for efficiency:
-
-```
-neural_field_data.h5
-├── mesh/
-│   ├── node_coordinates    [n_nodes, 3]
-│   └── node_ids            [n_nodes]
-└── results/
-    ├── displacement        [n_nodes, 6]
-    ├── displacement_node_ids
-    ├── stress/
-    │   ├── ctetra_stress/
-    │   │   ├── data        [n_elem, n_components]
-    │   │   └── element_ids
-    │   └── cquad4_stress/...
-    ├── strain/...
-    └── reactions/...
-```
-
-## Adding Design Metadata
-
-Create a `metadata.json` in each case directory to track design parameters:
-
-```json
-{
-  "design_parameters": {
-    "thickness": 2.5,
-    "fillet_radius": 5.0,
-    "rib_height": 15.0
-  },
-  "optimization_context": {
-    "objectives": ["minimize_weight", "minimize_stress"],
-    "constraints": ["max_displacement < 2mm"],
-    "iteration": 42
-  },
-  "notes": "Baseline design with standard loading"
-}
-```
-
-See [metadata_template.json](metadata_template.json) for a complete template.
-
-## Preparing NX Nastran Analyses
-
-### Required Output Requests
-
-Add these to your Nastran input deck or NX solution setup:
-
-```nastran
-DISPLACEMENT = ALL
-STRESS = ALL
-STRAIN = ALL
-SPCFORCES = ALL
-```
-
-### Recommended Settings
-
-- **Element Types**: CTETRA10, CHEXA20, CQUAD4
-- **Analysis**: SOL 101 (Linear Static) initially
-- **Units**: Consistent (recommend SI: mm, N, MPa, kg)
-- **Output Format**: OP2 (binary) for efficiency
-
-### Common Issues
-
-**"OP2 has no results"**
-- Ensure analysis completed successfully (check .log file)
-- Verify output requests (DISPLACEMENT=ALL, STRESS=ALL)
-- Check that OP2 file is not empty (should be > 1 KB)
-
-**"Can't find BDF nodes"**
-- Use .bdf or .dat file, not .sim
-- Ensure mesh was exported to solver deck
-- Check that BDF contains GRID cards
-
-**"Memory error with large models"**
-- Parser uses HDF5 chunking and compression
-- For models > 100k elements, ensure you have sufficient RAM
-- Consider splitting into subcases
-
-## Loading Parsed Data
-
-### In Python
-
-```python
-import json
-import h5py
-import numpy as np
-
-# Load metadata
-with open("neural_field_data.json", 'r') as f:
-    metadata = json.load(f)
-
-# Load field data
-with h5py.File("neural_field_data.h5", 'r') as f:
-    # Get node coordinates
-    coords = f['mesh/node_coordinates'][:]
-
-    # Get displacement field
-    displacement = f['results/displacement'][:]
-
-    # Get stress field
-    stress = f['results/stress/ctetra_stress/data'][:]
-    stress_elem_ids = f['results/stress/ctetra_stress/element_ids'][:]
-```
-
-### In PyTorch (for neural network training)
-
-```python
-import torch
-from torch.utils.data import Dataset
-
-class NeuralFieldDataset(Dataset):
-    def __init__(self, case_directories):
-        self.cases = []
-        for case_dir in case_directories:
-            h5_file = f"{case_dir}/neural_field_data.h5"
-            with h5py.File(h5_file, 'r') as f:
-                # Load inputs (mesh, BCs, loads)
-                coords = torch.from_numpy(f['mesh/node_coordinates'][:])
-
-                # Load outputs (displacement, stress fields)
-                displacement = torch.from_numpy(f['results/displacement'][:])
-
-                self.cases.append({
-                    'coords': coords,
-                    'displacement': displacement
-                })
-
-    def __len__(self):
-        return len(self.cases)
-
-    def __getitem__(self, idx):
-        return self.cases[idx]
-```
-
-## Architecture & Design
-
-### Why This Format?
-
-1. **Complete Fields, Not Scalars**: Neural networks need to learn how stress/displacement varies across the entire structure, not just maximum values.
-
-2. **Separation of Concerns**: JSON for structure/metadata (human-readable), HDF5 for numerical data (efficient).
-
-3. **Future-Proof**: Versioned format allows adding new fields without breaking existing data.
-
-4. **Physics Preservation**: Maintains all physics relationships (mesh topology, BCs, loads → results).
-
-### Integration with Atomizer
-
-This parser is Phase 1 of AtomizerField. Future integration:
-- Phase 2: Neural network architecture (Graph Neural Networks)
-- Phase 3: Training pipeline with physics-informed loss functions
-- Phase 4: Integration with main Atomizer dashboard
-- Phase 5: Production deployment for real-time optimization
-
-## Troubleshooting
-
-### Parser Errors
-
-| Error | Solution |
-|-------|----------|
-| `FileNotFoundError: No model.bdf found` | Ensure BDF/DAT file exists in `input/` directory |
-| `FileNotFoundError: No model.op2 found` | Ensure OP2 file exists in `output/` directory |
-| `pyNastran read error` | Check BDF syntax, try opening in text editor |
-| `OP2 subcase not found` | Ensure analysis ran successfully, check .f06 file |
-
-### Validation Warnings
-
-| Warning | Meaning | Action |
-|---------|---------|--------|
-| `No SPCs defined` | Model may be unconstrained | Check boundary conditions |
-| `No loads defined` | Model has no loading | Add forces, pressures, or gravity |
-| `Zero displacement` | Model not deforming | Check loads and constraints |
-| `Very large displacement` | Possible rigid body motion | Add constraints or check units |
-
-### Data Quality Issues
-
-**NaN or Inf values:**
-- Usually indicates analysis convergence failure
-- Check .f06 file for error messages
-- Verify model is properly constrained
-
-**Mismatch in node counts:**
-- Some nodes may not have results (e.g., rigid elements)
-- Check element connectivity
-- Validate mesh quality in NX
-
-## Example Workflow
-
-Here's a complete example workflow from FEA to neural network training data:
-
-### 1. Create Parametric Study in NX
-
-```bash
-# Generate 10 design variants with different thicknesses
-# Run each analysis with SOL 101
-# Export BDF and OP2 files for each
-```
-
-### 2. Organize Files
-
-```bash
-mkdir parametric_study
-for i in {1..10}; do
-    mkdir -p parametric_study/thickness_${i}/input
-    mkdir -p parametric_study/thickness_${i}/output
-    # Copy BDF and OP2 files
-done
-```
-
-### 3. Batch Parse
-
-```bash
-python batch_parser.py parametric_study
-```
-
-### 4. Review Results
-
-```bash
-# Check summary
-cat parametric_study/batch_processing_summary.json
-
-# Validate a specific case
-python validate_parsed_data.py parametric_study/thickness_5
-```
-
-### 5. Load into Neural Network
-
-```python
-from torch.utils.data import DataLoader
-
-dataset = NeuralFieldDataset([
-    f"parametric_study/thickness_{i}" for i in range(1, 11)
-])
-
-dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
-
-# Ready for training!
-```
-
-## Performance
-
-Typical parsing times (on standard laptop):
-- Small model (1k elements): ~5 seconds
-- Medium model (10k elements): ~15 seconds
-- Large model (100k elements): ~60 seconds
-- Very large (1M elements): ~10 minutes
-
-File sizes (compressed HDF5):
-- Mesh (100k nodes): ~10 MB
-- Displacement field (100k nodes × 6 DOF): ~5 MB
-- Stress field (100k elements × 10 components): ~8 MB
-
-## Requirements
-
-- Python 3.8+
-- pyNastran 1.4+
-- NumPy 1.20+
-- h5py 3.0+
-- NX Nastran (any version that outputs .bdf and .op2)
-
-## Files in This Repository
-
-| File | Purpose |
-|------|---------|
-| `neural_field_parser.py` | Main parser - BDF/OP2 to neural field format |
-| `validate_parsed_data.py` | Data validation and quality checks |
-| `batch_parser.py` | Batch processing for multiple cases |
-| `metadata_template.json` | Template for design parameter tracking |
-| `requirements.txt` | Python dependencies |
-| `README.md` | This file |
-| `Context.md` | Project context and vision |
-| `Instructions.md` | Original implementation instructions |
-
-## Development
-
-### Testing with Example Models
-
-There are example models in the `Models/` folder. To test the parser:
-
-```bash
-# Set up test case
-mkdir test_case_001
-mkdir test_case_001/input
-mkdir test_case_001/output
-
-# Copy example files
-cp Models/example_model.bdf test_case_001/input/model.bdf
-cp Models/example_model.op2 test_case_001/output/model.op2
-
-# Run parser
-python neural_field_parser.py test_case_001
-
-# Validate
-python validate_parsed_data.py test_case_001
-```
-
-### Extending the Parser
-
-To add new result types (e.g., modal analysis, thermal):
-
-1. Update `extract_results()` in `neural_field_parser.py`
-2. Add corresponding validation in `validate_parsed_data.py`
-3. Update data structure version if needed
-4. Document changes in this README
-
-### Contributing
-
-This is part of the AtomizerField project. When making changes:
-- Preserve the v1.0 data format for backwards compatibility
-- Add comprehensive error handling
-- Update validation checks accordingly
-- Test with multiple element types
-- Document physics assumptions
-
-## Future Enhancements
-
-Planned features:
-- [ ] Support for nonlinear analyses (SOL 106)
-- [ ] Modal analysis results (SOL 103)
-- [ ] Thermal analysis (SOL 153)
-- [ ] Contact results
-- [ ] Composite material support
-- [ ] Automatic mesh quality assessment
-- [ ] Parallel batch processing
-- [ ] Progress bars for long operations
-- [ ] Integration with Atomizer dashboard
-
-## License
-
-Part of the Atomizer optimization platform.
-
-## Support
-
-For issues or questions:
-1. Check this README and troubleshooting section
-2. Review `Context.md` for project background
-3. Examine example files in `Models/` folder
-4. Check pyNastran documentation for BDF/OP2 specifics
-
-## Version History
-
-### v1.0.0 (Current)
-- Initial release
-- Complete BDF/OP2 parsing
-- Support for solid, shell, beam elements
-- HDF5 + JSON output format
-- Data validation
-- Batch processing
-- Physics consistency checks
-
----
-
-**AtomizerField**: Revolutionizing structural optimization through neural field learning.
-
-*Built with Claude Code, designed for the future of engineering.*

atomizer-field/SIMPLE_BEAM_TEST_REPORT.md

@@ -1,529 +0,0 @@
-# Simple Beam Test Report
-
-**AtomizerField Neural Field Learning System**
-
-**Test Date:** November 24, 2025
-**Model:** Simple Beam (beam_sim1-solution_1)
-**Status:** ✅ ALL TESTS PASSED
-
----
-
-## Executive Summary
-
-The AtomizerField system has been successfully validated with your actual Simple Beam FEA model. All 7 comprehensive tests passed, demonstrating complete functionality from BDF/OP2 parsing through neural network prediction.
-
-**Key Results:**
-- ✅ 7/7 tests passed
-- ✅ 5,179 nodes processed
-- ✅ 4,866 elements parsed
-- ✅ Complete field extraction (displacement + stress)
-- ✅ Neural network inference: 95.94 ms
-- ✅ System ready for training!
-
----
-
-## Test Results
-
-### Test 1: File Existence ✅ PASS
-**Purpose:** Verify Simple Beam files are available
-
-**Results:**
-- BDF file found: `beam_sim1-solution_1.dat` (1,230.1 KB)
-- OP2 file found: `beam_sim1-solution_1.op2` (4,461.2 KB)
-
-**Status:** Files located and validated
-
----
-
-### Test 2: Directory Setup ✅ PASS
-**Purpose:** Create test case directory structure
-
-**Results:**
-- Created: `test_case_beam/input/`
-- Created: `test_case_beam/output/`
-- Copied BDF to input directory
-- Copied OP2 to output directory
-
-**Status:** Directory structure established
-
----
-
-### Test 3: Module Imports ✅ PASS
-**Purpose:** Verify all required modules load correctly
-
-**Results:**
-- pyNastran imported successfully
-- AtomizerField parser imported successfully
-- All dependencies available
-
-**Status:** Environment configured correctly
-
----
-
-### Test 4: BDF/OP2 Parsing ✅ PASS
-**Purpose:** Extract all data from FEA files
-
-**Parse Time:** 1.27 seconds
-
-**Extracted Data:**
-- **Nodes:** 5,179 nodes with 3D coordinates
-- **Elements:** 4,866 CQUAD4 shell elements
-- **Materials:** 1 material definition
-- **Boundary Conditions:** 0 SPCs, 0 MPCs
-- **Loads:** 35 forces, 0 pressures, 0 gravity, 0 thermal
-- **Displacement Field:** 5,179 nodes × 6 DOF
-  - Maximum displacement: 19.556875 mm
-- **Stress Field:** 9,732 stress values (2 per element)
-  - Captured for all elements
-- **Reactions:** 5,179 reaction forces
-  - Maximum force: 152,198,576 N
-
-**Output Files:**
-- JSON metadata: 1,686.3 KB
-- HDF5 field data: 546.3 KB
-- **Total:** 2,232.6 KB
-
-**Status:** Complete field extraction successful
-
----
-
-### Test 5: Data Validation ✅ PASS
-**Purpose:** Verify data quality and physics consistency
-
-**Validation Checks:**
-- ✅ JSON and HDF5 files present
-- ✅ All required fields found
-- ✅ Node coordinates valid (5,179 nodes)
-- ✅ Element connectivity valid (4,866 elements)
-- ✅ Material definitions complete (1 material)
-- ✅ Displacement field complete (max: 19.56 mm)
-- ✅ Stress field complete (9,732 values)
-- ⚠ Warning: No SPCs defined (may be unconstrained)
-
-**Status:** Data quality validated, ready for neural network
-
----
-
-### Test 6: Graph Conversion ✅ PASS
-**Purpose:** Convert to PyTorch Geometric format for neural network
-
-**Graph Structure:**
-- **Nodes:** 5,179 nodes
-- **Node Features:** 12 dimensions
-  - Position (3D)
-  - Boundary conditions (6 DOF)
-  - Applied loads (3D)
-- **Edges:** 58,392 edges
-- **Edge Features:** 5 dimensions
-  - Young's modulus
-  - Poisson's ratio
-  - Density
-  - Shear modulus
-  - Thermal expansion
-- **Target Displacement:** (5179, 6) - 6 DOF per node
-- **Target Stress:** (9732, 8) - Full stress tensor per element
-
-**Status:** Successfully converted to graph neural network format
-
----
-
-### Test 7: Neural Prediction ✅ PASS
-**Purpose:** Validate neural network can process the data
-
-**Model Configuration:**
-- Architecture: Graph Neural Network (GNN)
-- Parameters: 128,589 parameters
-- Layers: 6 message passing layers
-- Hidden dimension: 64
-- Model state: Untrained (random weights)
-
-**Inference Performance:**
-- **Inference Time:** 95.94 ms
-- **Target:** < 100 ms ✅
-- **Speedup vs FEA:** 1000× expected after training
-
-**Predictions (Untrained Model):**
-- Max displacement: 2.03 (arbitrary units)
-- Max stress: 4.98 (arbitrary units)
-
-**Note:** Values are from untrained model with random weights. After training on 50-500 examples, predictions will match FEA results with < 10% error.
-
-**Status:** Neural network architecture validated and functional
-
----
-
-## Model Statistics
-
-### Geometry
-| Property | Value |
-|----------|-------|
-| Nodes | 5,179 |
-| Elements | 4,866 |
-| Element Type | CQUAD4 (shell) |
-| Materials | 1 |
-
-### Loading
-| Property | Value |
-|----------|-------|
-| Applied Forces | 35 |
-| Pressure Loads | 0 |
-| Gravity Loads | 0 |
-| Thermal Loads | 0 |
-
-### Results
-| Property | Value |
-|----------|-------|
-| Max Displacement | 19.556875 mm |
-| Displacement Nodes | 5,179 |
-| Stress Elements | 9,732 (2 per element) |
-| Max Reaction Force | 152,198,576 N |
-
-### Data Files
-| File | Size |
-|------|------|
-| BDF Input | 1,230.1 KB |
-| OP2 Results | 4,461.2 KB |
-| JSON Metadata | 1,686.3 KB |
-| HDF5 Field Data | 546.3 KB |
-| **Total Parsed** | **2,232.6 KB** |
-
----
-
-## 3D Visualizations
-
-### Mesh Structure
-![Mesh Structure](visualization_images/mesh.png)
-
-The Simple Beam model consists of 5,179 nodes connected by 4,866 CQUAD4 shell elements, creating a detailed 3D representation of the beam geometry.
-
-### Displacement Field
-![Displacement Field](visualization_images/displacement.png)
-
-**Left:** Original mesh
-**Right:** Deformed mesh (10× displacement scale)
-
-The displacement field shows the beam's deformation under load, with maximum displacement of 19.56 mm. Colors represent displacement magnitude, with red indicating maximum deformation.
-
-### Stress Field
-![Stress Field](visualization_images/stress.png)
-
-The von Mises stress distribution shows stress concentrations throughout the beam structure. Colors range from blue (low stress) to red (high stress), revealing critical stress regions.
-
----
-
-## Performance Metrics
-
-### Parsing Performance
-| Metric | Value |
-|--------|-------|
-| Parse Time | 1.27 seconds |
-| Nodes/second | 4,077 nodes/s |
-| Elements/second | 3,831 elements/s |
-
-### Neural Network Performance
-| Metric | Value | Target | Status |
-|--------|-------|--------|--------|
-| Inference Time | 95.94 ms | < 100 ms | ✅ Pass |
-| Model Parameters | 128,589 | - | - |
-| Forward Pass | Working | - | ✅ |
-| Gradient Flow | Working | - | ✅ |
-
-### Comparison: FEA vs Neural (After Training)
-| Operation | FEA Time | Neural Time | Speedup |
-|-----------|----------|-------------|---------|
-| Single Analysis | 30-300 s | 0.096 s | **300-3000×** |
-| Optimization (100 evals) | 50-500 min | 10 s | **300-3000×** |
-| Gradient Computation | Very slow | 0.1 ms | **1,000,000×** |
-
----
-
-## System Validation
-
-### Functional Tests
-- ✅ File I/O (BDF/OP2 reading)
-- ✅ Data extraction (mesh, materials, BCs, loads)
-- ✅ Field extraction (displacement, stress)
-- ✅ Data validation (quality checks)
-- ✅ Format conversion (FEA → neural)
-- ✅ Graph construction (PyTorch Geometric)
-- ✅ Neural network inference
-
-### Data Quality
-- ✅ No NaN values in coordinates
-- ✅ No NaN values in displacement
-- ✅ No NaN values in stress
-- ✅ Element connectivity valid
-- ✅ Node IDs consistent
-- ✅ Physics units preserved (mm, MPa, N)
-
-### Neural Network
-- ✅ Model instantiation
-- ✅ Forward pass
-- ✅ All 4 loss functions operational
-- ✅ Batch processing
-- ✅ Gradient computation
-
----
-
-## Next Steps
-
-### 1. Generate Training Data (50-500 cases)
-**Goal:** Create diverse dataset for training
-
-**Approach:**
-- Vary beam dimensions
-- Vary loading conditions
-- Vary material properties
-- Vary boundary conditions
-
-**Command:**
-```bash
-conda activate atomizer_field
-python batch_parser.py --input Models/ --output training_data/
-```
-
-### 2. Train Neural Network
-**Goal:** Learn FEA behavior from examples
-
-**Configuration:**
-- Epochs: 100-200
-- Batch size: 16
-- Learning rate: 0.001
-- Loss: Physics-informed
-
-**Command:**
-```bash
-python train.py \
-  --data_dirs training_data/* \
-  --epochs 100 \
-  --batch_size 16 \
-  --loss physics \
-  --checkpoint_dir checkpoints/
-```
-
-**Expected Training Time:** 2-6 hours (GPU recommended)
-
-### 3. Validate Performance
-**Goal:** Verify < 10% prediction error
-
-**Tests:**
-- Physics validation (cantilever, beam tests)
-- Learning tests (memorization, interpolation)
-- Prediction accuracy on test set
-
-**Command:**
-```bash
-python test_suite.py --full
-```
-
-### 4. Deploy to Production
-**Goal:** Integrate with Atomizer for optimization
-
-**Integration:**
-```python
-from optimization_interface import NeuralFieldOptimizer
-
-# Initialize
-optimizer = NeuralFieldOptimizer('checkpoints/best_model.pt')
-
-# Replace FEA calls
-results = optimizer.evaluate(design_graph)
-gradients = optimizer.get_sensitivities(design_graph)
-```
-
-**Expected Speedup:** 1000× faster than FEA!
-
----
-
-## Technical Details
-
-### Graph Neural Network Architecture
-
-**Input Layer:**
-- Node features: 12D (position, BCs, loads)
-- Edge features: 5D (material properties)
-
-**Hidden Layers:**
-- 6 message passing layers
-- Hidden dimension: 64
-- Activation: ReLU
-- Dropout: 0.1
-
-**Output Layers:**
-- Displacement decoder: 6 DOF per node
-- Stress predictor: 6 stress components per element
-- Von Mises calculator: Scalar per element
-
-**Total Parameters:** 128,589
-
-### Data Format
-
-**JSON Metadata:**
-```json
-{
-  "metadata": { "case_name", "analysis_type", ... },
-  "mesh": { "nodes", "elements", "statistics" },
-  "materials": { ... },
-  "boundary_conditions": { ... },
-  "loads": { ... },
-  "results": { "displacement", "stress" }
-}
-```
-
-**HDF5 Arrays:**
-- `mesh/node_coordinates`: (5179, 3) float32
-- `mesh/node_ids`: (5179,) int32
-- `results/displacement`: (5179, 6) float32
-- `results/stress/cquad4_stress/data`: (9732, 8) float32
-
-### Physics-Informed Loss
-
-**Total Loss:**
-```
-L_total = λ_data * L_data
-        + λ_equilibrium * L_equilibrium
-        + λ_constitutive * L_constitutive
-        + λ_boundary * L_boundary
-```
-
-**Components:**
-- **Data Loss:** MSE between prediction and FEA
-- **Equilibrium:** ∇·σ + f = 0 (force balance)
-- **Constitutive:** σ = C:ε (Hooke's law)
-- **Boundary:** Enforce BC compliance
-
----
-
-## Conclusions
-
-### ✅ System Status: FULLY OPERATIONAL
-
-All components of the AtomizerField system have been validated:
-
-1. **Data Pipeline** ✅
-   - BDF/OP2 parsing working
-   - Complete field extraction
-   - Data quality validated
-
-2. **Neural Network** ✅
-   - Model architecture validated
-   - Forward pass working
-   - Inference time: 95.94 ms
-
-3. **Visualization** ✅
-   - 3D mesh rendering
-   - Displacement fields
-   - Stress fields
-   - Automated report generation
-
-4. **Testing Framework** ✅
-   - 7/7 tests passing
-   - Comprehensive validation
-   - Performance benchmarks met
-
-### Key Achievements
-
-- ✅ Successfully parsed real 5,179-node model
-- ✅ Extracted complete displacement and stress fields
-- ✅ Converted to neural network format
-- ✅ Neural inference < 100ms
-- ✅ 3D visualization working
-- ✅ Ready for training!
-
-### Performance Expectations
-
-**After Training (50-500 cases, 100-200 epochs):**
-- Prediction error: < 10% vs FEA
-- Inference time: 5-50 ms
-- Speedup: 1000× faster than FEA
-- Optimization: 1,000,000× faster gradients
-
-### Production Readiness
-
-The system is **ready for production** after training:
-- ✅ All tests passing
-- ✅ Data pipeline validated
-- ✅ Neural architecture proven
-- ✅ Visualization tools available
-- ✅ Integration interface ready
-
-**The AtomizerField system will revolutionize your structural optimization workflow with 1000× faster predictions!** 🚀
-
----
-
-## Appendix
-
-### Files Generated
-
-**Test Data:**
-- `test_case_beam/input/model.bdf` (1,230 KB)
-- `test_case_beam/output/model.op2` (4,461 KB)
-- `test_case_beam/neural_field_data.json` (1,686 KB)
-- `test_case_beam/neural_field_data.h5` (546 KB)
-
-**Visualizations:**
-- `visualization_images/mesh.png` (227 KB)
-- `visualization_images/displacement.png` (335 KB)
-- `visualization_images/stress.png` (215 KB)
-
-**Reports:**
-- `visualization_report.md`
-- `SIMPLE_BEAM_TEST_REPORT.md` (this file)
-
-### Commands Reference
-
-```bash
-# Activate environment
-conda activate atomizer_field
-
-# Run tests
-python test_simple_beam.py                    # Simple Beam test
-python test_suite.py --quick                  # Smoke tests
-python test_suite.py --full                   # Complete validation
-
-# Visualize
-python visualize_results.py test_case_beam --mesh          # Mesh only
-python visualize_results.py test_case_beam --displacement  # Displacement
-python visualize_results.py test_case_beam --stress        # Stress
-python visualize_results.py test_case_beam --report        # Full report
-
-# Parse data
-python neural_field_parser.py test_case_beam  # Single case
-python batch_parser.py --input Models/        # Batch
-
-# Train
-python train.py --data_dirs training_data/* --epochs 100
-
-# Predict
-python predict.py --model best_model.pt --data test_case/
-```
-
-### Environment Details
-
-**Conda Environment:** `atomizer_field`
-
-**Key Packages:**
-- Python 3.10.19
-- NumPy 1.26.4 (conda-compiled)
-- PyTorch 2.5.1
-- PyTorch Geometric 2.7.0
-- pyNastran 1.4.1
-- Matplotlib 3.10.7
-- H5Py 3.15.1
-
-**Installation:**
-```bash
-conda create -n atomizer_field python=3.10 numpy scipy -y
-conda activate atomizer_field
-conda install pytorch torchvision torchaudio cpuonly -c pytorch -y
-pip install torch-geometric pyNastran h5py tensorboard matplotlib
-```
-
----
-
-**Report Generated:** November 24, 2025
-**AtomizerField Version:** 1.0
-**Status:** ✅ All Systems Operational
-**Ready For:** Production Training and Deployment
-
-🎉 **COMPLETE SUCCESS!**

atomizer-field/SYSTEM_ARCHITECTURE.md

@@ -1,741 +0,0 @@
-# AtomizerField - Complete System Architecture
-
-## 📍 Project Location
-
-```
-c:\Users\antoi\Documents\Atomaste\Atomizer-Field\
-```
-
-## 🏗️ System Overview
-
-AtomizerField is a **two-phase system** that transforms FEA results into neural network predictions:
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                        PHASE 1: DATA PIPELINE                    │
-├─────────────────────────────────────────────────────────────────┤
-│                                                                  │
-│  NX Nastran Files (.bdf, .op2)                                  │
-│           ↓                                                      │
-│  neural_field_parser.py                                         │
-│           ↓                                                      │
-│  Neural Field Format (JSON + HDF5)                              │
-│           ↓                                                      │
-│  validate_parsed_data.py                                        │
-│                                                                  │
-└─────────────────────────────────────────────────────────────────┘
-                            ↓
-┌─────────────────────────────────────────────────────────────────┐
-│                    PHASE 2: NEURAL NETWORK                       │
-├─────────────────────────────────────────────────────────────────┤
-│                                                                  │
-│  data_loader.py → Graph Representation                          │
-│           ↓                                                      │
-│  train.py + field_predictor.py (GNN)                           │
-│           ↓                                                      │
-│  Trained Model (checkpoint_best.pt)                             │
-│           ↓                                                      │
-│  predict.py → Field Predictions (5-50ms!)                       │
-│                                                                  │
-└─────────────────────────────────────────────────────────────────┘
-```
-
----
-
-## 📂 Complete File Structure
-
-```
-Atomizer-Field/
-│
-├── 📄 Core Documentation
-│   ├── README.md                    # Phase 1 detailed guide
-│   ├── PHASE2_README.md             # Phase 2 detailed guide
-│   ├── GETTING_STARTED.md           # Quick start tutorial
-│   ├── SYSTEM_ARCHITECTURE.md       # This file (system overview)
-│   ├── Context.md                   # Project vision & philosophy
-│   └── Instructions.md              # Original implementation spec
-│
-├── 🔧 Phase 1: FEA Data Parser
-│   ├── neural_field_parser.py       # Main parser (BDF/OP2 → Neural format)
-│   ├── validate_parsed_data.py      # Data quality validation
-│   ├── batch_parser.py              # Batch processing multiple cases
-│   └── metadata_template.json       # Template for design parameters
-│
-├── 🧠 Phase 2: Neural Network
-│   ├── neural_models/
-│   │   ├── __init__.py
-│   │   ├── field_predictor.py       # GNN architecture (718K params)
-│   │   ├── physics_losses.py        # Physics-informed loss functions
-│   │   └── data_loader.py           # PyTorch Geometric data pipeline
-│   │
-│   ├── train.py                     # Training script
-│   └── predict.py                   # Inference script
-│
-├── 📦 Dependencies & Config
-│   ├── requirements.txt             # All dependencies
-│   └── .gitignore                   # (if using git)
-│
-├── 📁 Data Directories (created during use)
-│   ├── training_data/               # Parsed training cases
-│   ├── validation_data/             # Parsed validation cases
-│   ├── test_data/                   # Parsed test cases
-│   └── runs/                        # Training outputs
-│       ├── checkpoint_best.pt       # Best model
-│       ├── checkpoint_latest.pt     # Latest checkpoint
-│       ├── config.json              # Model configuration
-│       └── tensorboard/             # Training logs
-│
-├── 🔬 Example Models (your existing data)
-│   └── Models/
-│       └── Simple Beam/
-│           ├── beam_sim1-solution_1.dat  # BDF file
-│           ├── beam_sim1-solution_1.op2  # OP2 results
-│           └── ...
-│
-└── 🐍 Virtual Environment
-    └── atomizer_env/                # Python virtual environment
-```
-
----
-
-## 🔍 PHASE 1: Data Parser - Deep Dive
-
-### Location
-```
-c:\Users\antoi\Documents\Atomaste\Atomizer-Field\neural_field_parser.py
-```
-
-### What It Does
-
-**Transforms this:**
-```
-NX Nastran Files:
-├── model.bdf  (1.2 MB text file with mesh, materials, BCs, loads)
-└── model.op2  (4.5 MB binary file with stress/displacement results)
-```
-
-**Into this:**
-```
-Neural Field Format:
-├── neural_field_data.json  (200 KB - metadata, structure)
-└── neural_field_data.h5    (3 MB - large numerical arrays)
-```
-
-### Data Structure Breakdown
-
-#### 1. JSON File (neural_field_data.json)
-```json
-{
-  "metadata": {
-    "version": "1.0.0",
-    "created_at": "2024-01-15T10:30:00",
-    "source": "NX_Nastran",
-    "case_name": "training_case_001",
-    "analysis_type": "SOL_101",
-    "units": {
-      "length": "mm",
-      "force": "N",
-      "stress": "MPa"
-    },
-    "file_hashes": {
-      "bdf": "sha256_hash_here",
-      "op2": "sha256_hash_here"
-    }
-  },
-
-  "mesh": {
-    "statistics": {
-      "n_nodes": 15432,
-      "n_elements": 8765,
-      "element_types": {
-        "solid": 5000,
-        "shell": 3000,
-        "beam": 765
-      }
-    },
-    "bounding_box": {
-      "min": [0.0, 0.0, 0.0],
-      "max": [100.0, 50.0, 30.0]
-    },
-    "nodes": {
-      "ids": [1, 2, 3, ...],
-      "coordinates": "<stored in HDF5>",
-      "shape": [15432, 3]
-    },
-    "elements": {
-      "solid": [
-        {
-          "id": 1,
-          "type": "CTETRA",
-          "nodes": [1, 5, 12, 34],
-          "material_id": 1,
-          "property_id": 10
-        },
-        ...
-      ],
-      "shell": [...],
-      "beam": [...]
-    }
-  },
-
-  "materials": [
-    {
-      "id": 1,
-      "type": "MAT1",
-      "E": 71700.0,        // Young's modulus (MPa)
-      "nu": 0.33,          // Poisson's ratio
-      "rho": 2.81e-06,     // Density (kg/mm³)
-      "G": 26900.0,        // Shear modulus (MPa)
-      "alpha": 2.3e-05     // Thermal expansion (1/°C)
-    }
-  ],
-
-  "boundary_conditions": {
-    "spc": [              // Single-point constraints
-      {
-        "id": 1,
-        "node": 1,
-        "dofs": "123456",  // Constrained DOFs (x,y,z,rx,ry,rz)
-        "enforced_motion": 0.0
-      },
-      ...
-    ],
-    "mpc": []             // Multi-point constraints
-  },
-
-  "loads": {
-    "point_forces": [
-      {
-        "id": 100,
-        "type": "force",
-        "node": 500,
-        "magnitude": 10000.0,  // Newtons
-        "direction": [1.0, 0.0, 0.0],
-        "coord_system": 0
-      }
-    ],
-    "pressure": [],
-    "gravity": [],
-    "thermal": []
-  },
-
-  "results": {
-    "displacement": {
-      "node_ids": [1, 2, 3, ...],
-      "data": "<stored in HDF5>",
-      "shape": [15432, 6],
-      "max_translation": 0.523456,
-      "max_rotation": 0.001234,
-      "units": "mm and radians"
-    },
-    "stress": {
-      "ctetra_stress": {
-        "element_ids": [1, 2, 3, ...],
-        "data": "<stored in HDF5>",
-        "shape": [5000, 7],
-        "max_von_mises": 245.67,
-        "units": "MPa"
-      }
-    }
-  }
-}
-```
-
-#### 2. HDF5 File (neural_field_data.h5)
-
-**Structure:**
-```
-neural_field_data.h5
-│
-├── /mesh/
-│   ├── node_coordinates        [15432 × 3]  float64
-│   │   Each row: [x, y, z] in mm
-│   │
-│   └── node_ids                [15432]      int32
-│       Node ID numbers
-│
-└── /results/
-    ├── /displacement           [15432 × 6]  float64
-    │   Each row: [ux, uy, uz, θx, θy, θz]
-    │   Translation (mm) + Rotation (radians)
-    │
-    ├── displacement_node_ids   [15432]      int32
-    │
-    ├── /stress/
-    │   ├── /ctetra_stress/
-    │   │   ├── data            [5000 × 7]   float64
-    │   │   │   [σxx, σyy, σzz, τxy, τyz, τxz, von_mises]
-    │   │   └── element_ids     [5000]       int32
-    │   │
-    │   └── /cquad4_stress/
-    │       └── ...
-    │
-    ├── /strain/
-    │   └── ...
-    │
-    └── /reactions              [N × 6]      float64
-        Reaction forces at constrained nodes
-```
-
-**Why HDF5?**
-- ✅ Efficient storage (compressed)
-- ✅ Fast random access
-- ✅ Handles large arrays (millions of values)
-- ✅ Industry standard for scientific data
-- ✅ Direct NumPy/PyTorch integration
-
-### Parser Code Flow
-
-```python
-# neural_field_parser.py - Main Parser Class
-
-class NastranToNeuralFieldParser:
-    def __init__(self, case_directory):
-        # Find BDF and OP2 files
-        # Initialize pyNastran readers
-
-    def parse_all(self):
-        # 1. Read BDF (input deck)
-        self.bdf.read_bdf(bdf_file)
-
-        # 2. Read OP2 (results)
-        self.op2.read_op2(op2_file)
-
-        # 3. Extract data
-        self.extract_metadata()      # Analysis info, units
-        self.extract_mesh()          # Nodes, elements, connectivity
-        self.extract_materials()     # Material properties
-        self.extract_boundary_conditions()  # SPCs, MPCs
-        self.extract_loads()         # Forces, pressures, gravity
-        self.extract_results()       # COMPLETE FIELDS (key!)
-
-        # 4. Save
-        self.save_data()             # JSON + HDF5
-```
-
-**Key Innovation in `extract_results()`:**
-```python
-def extract_results(self):
-    # Traditional FEA post-processing:
-    # max_stress = np.max(stress_data)  ← LOSES SPATIAL INFO!
-
-    # AtomizerField approach:
-    # Store COMPLETE field at EVERY node/element
-    results["displacement"] = {
-        "data": disp_data.tolist(),  # ALL 15,432 nodes × 6 DOF
-        "shape": [15432, 6],
-        "max_translation": float(np.max(magnitudes))  # Also store max
-    }
-
-    # This enables neural network to learn spatial patterns!
-```
-
----
-
-## 🧠 PHASE 2: Neural Network - Deep Dive
-
-### Location
-```
-c:\Users\antoi\Documents\Atomaste\Atomizer-Field\neural_models\
-```
-
-### Architecture Overview
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                    AtomizerFieldModel                            │
-│                      (718,221 parameters)                        │
-├─────────────────────────────────────────────────────────────────┤
-│                                                                  │
-│  INPUT: Graph Representation of FEA Mesh                        │
-│  ├── Nodes (15,432):                                            │
-│  │   └── Features [12D]: [x,y,z, BC_mask(6), loads(3)]        │
-│  └── Edges (mesh connectivity):                                 │
-│      └── Features [5D]: [E, ν, ρ, G, α] (materials)           │
-│                                                                  │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │  NODE ENCODER (12 → 128)                         │          │
-│  │  Embeds node position + BCs + loads              │          │
-│  └──────────────────────────────────────────────────┘          │
-│                        ↓                                        │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │  EDGE ENCODER (5 → 64)                           │          │
-│  │  Embeds material properties                       │          │
-│  └──────────────────────────────────────────────────┘          │
-│                        ↓                                        │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │  MESSAGE PASSING LAYERS × 6                      │          │
-│  │  ┌────────────────────────────────────┐          │          │
-│  │  │  Layer 1: MeshGraphConv            │          │          │
-│  │  │  ├── Gather neighbor info          │          │          │
-│  │  │  ├── Combine with edge features    │          │          │
-│  │  │  ├── Update node representations   │          │          │
-│  │  │  └── Residual + LayerNorm          │          │          │
-│  │  ├────────────────────────────────────┤          │          │
-│  │  │  Layer 2-6: Same structure         │          │          │
-│  │  └────────────────────────────────────┘          │          │
-│  │  (Forces propagate through mesh!)                │          │
-│  └──────────────────────────────────────────────────┘          │
-│                        ↓                                        │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │  DISPLACEMENT DECODER (128 → 6)                  │          │
-│  │  Predicts: [ux, uy, uz, θx, θy, θz]            │          │
-│  └──────────────────────────────────────────────────┘          │
-│                        ↓                                        │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │  STRESS PREDICTOR (6 → 6)                        │          │
-│  │  From displacement → stress tensor                │          │
-│  │  Outputs: [σxx, σyy, σzz, τxy, τyz, τxz]       │          │
-│  └──────────────────────────────────────────────────┘          │
-│                        ↓                                        │
-│  OUTPUT:                                                        │
-│  ├── Displacement field [15,432 × 6]                           │
-│  ├── Stress field [15,432 × 6]                                 │
-│  └── Von Mises stress [15,432 × 1]                             │
-│                                                                  │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-### Graph Representation
-
-**From Mesh to Graph:**
-
-```
-FEA Mesh:                          Graph:
-
-Node 1 ──── Element 1 ──── Node 2  Node 1 ──── Edge ──── Node 2
-  │                           │       │                      │
-  │                           │     Features:              Features:
-  Element 2              Element 3   [x,y,z,              [x,y,z,
-  │                           │       BC,loads]            BC,loads]
-  │                           │         │                    │
-Node 3 ──── Element 4 ──── Node 4     Edge                 Edge
-                                       │                    │
-                                   [E,ν,ρ,G,α]          [E,ν,ρ,G,α]
-```
-
-**Built by `data_loader.py`:**
-
-```python
-class FEAMeshDataset(Dataset):
-    def _build_graph(self, metadata, node_coords, displacement, stress):
-        # 1. Build node features
-        x = torch.cat([
-            node_coords,        # [N, 3] - position
-            bc_mask,            # [N, 6] - which DOFs constrained
-            load_features       # [N, 3] - applied forces
-        ], dim=-1)              # → [N, 12]
-
-        # 2. Build edges from element connectivity
-        for element in elements:
-            nodes = element['nodes']
-            # Fully connect nodes within element
-            for i, j in pairs(nodes):
-                edge_index.append([i, j])
-                edge_attr.append(material_props)
-
-        # 3. Create PyTorch Geometric Data object
-        data = Data(
-            x=x,                      # Node features
-            edge_index=edge_index,    # Connectivity
-            edge_attr=edge_attr,      # Material properties
-            y_displacement=displacement,  # Target (ground truth)
-            y_stress=stress          # Target (ground truth)
-        )
-
-        return data
-```
-
-### Physics-Informed Loss
-
-**Standard Neural Network:**
-```python
-loss = MSE(prediction, ground_truth)
-# Only learns to match training data
-```
-
-**AtomizerField (Physics-Informed):**
-```python
-loss = λ_data × MSE(prediction, ground_truth)
-     + λ_eq × EquilibriumViolation(stress)      # ∇·σ + f = 0
-     + λ_const × ConstitutiveLawError(stress, strain)  # σ = C:ε
-     + λ_bc × BoundaryConditionError(disp, BCs)  # u = 0 at fixed nodes
-
-# Learns physics, not just patterns!
-```
-
-**Benefits:**
-- Faster convergence
-- Better generalization to unseen cases
-- Physically plausible predictions
-- Needs less training data
-
-### Training Pipeline
-
-**`train.py` workflow:**
-
-```python
-# 1. Load data
-train_loader = create_dataloaders(train_cases, val_cases)
-
-# 2. Create model
-model = AtomizerFieldModel(
-    node_feature_dim=12,
-    hidden_dim=128,
-    num_layers=6
-)
-
-# 3. Training loop
-for epoch in range(num_epochs):
-    for batch in train_loader:
-        # Forward pass
-        predictions = model(batch)
-
-        # Compute loss
-        losses = criterion(predictions, targets)
-
-        # Backward pass
-        losses['total_loss'].backward()
-        optimizer.step()
-
-    # Validate
-    val_metrics = validate(val_loader)
-
-    # Save checkpoint if best
-    if val_loss < best_val_loss:
-        save_checkpoint('checkpoint_best.pt')
-
-    # TensorBoard logging
-    writer.add_scalar('Loss/train', train_loss, epoch)
-```
-
-**Outputs:**
-```
-runs/
-├── checkpoint_best.pt         # Best model (lowest validation loss)
-├── checkpoint_latest.pt       # Latest state (for resuming)
-├── config.json                # Model configuration
-└── tensorboard/               # Training logs
-    └── events.out.tfevents...
-```
-
-### Inference (Prediction)
-
-**`predict.py` workflow:**
-
-```python
-# 1. Load trained model
-model = load_model('checkpoint_best.pt')
-
-# 2. Load new case (mesh + BCs + loads, NO FEA solve!)
-data = load_case('new_design')
-
-# 3. Predict in milliseconds
-predictions = model(data)  # ~15ms
-
-# 4. Extract results
-displacement = predictions['displacement']  # [N, 6]
-stress = predictions['stress']              # [N, 6]
-von_mises = predictions['von_mises']        # [N]
-
-# 5. Get max values (like traditional FEA)
-max_disp = np.max(np.linalg.norm(displacement[:, :3], axis=1))
-max_stress = np.max(von_mises)
-
-print(f"Max displacement: {max_disp:.6f} mm")
-print(f"Max stress: {max_stress:.2f} MPa")
-```
-
-**Performance:**
-- Traditional FEA: 2-3 hours
-- AtomizerField: 15 milliseconds
-- **Speedup: ~480,000×**
-
----
-
-## 🎯 Key Innovations
-
-### 1. Complete Field Learning (Not Scalars)
-
-**Traditional Surrogate:**
-```python
-# Only learns one number per analysis
-max_stress = neural_net(design_parameters)
-```
-
-**AtomizerField:**
-```python
-# Learns ENTIRE FIELD (45,000 values)
-stress_field = neural_net(mesh_graph)
-# Knows WHERE stress occurs, not just max value!
-```
-
-### 2. Graph Neural Networks (Respect Topology)
-
-```
-Why GNNs?
-- FEA solves: K·u = f
-- K depends on mesh connectivity
-- GNN learns on mesh structure
-- Messages propagate like forces!
-```
-
-### 3. Physics-Informed Training
-
-```
-Standard NN: "Make output match training data"
-AtomizerField: "Match data AND obey physics laws"
-Result: Better with less data!
-```
-
----
-
-## 💾 Data Flow Example
-
-### Complete End-to-End Flow
-
-```
-1. Engineer creates bracket in NX
-   ├── Geometry: 100mm × 50mm × 30mm
-   ├── Material: Aluminum 7075-T6
-   ├── Mesh: 15,432 nodes, 8,765 elements
-   ├── BCs: Fixed at mounting holes
-   └── Load: 10,000 N tension
-
-2. Run FEA in NX Nastran
-   ├── Time: 2.5 hours
-   └── Output: model.bdf, model.op2
-
-3. Parse to neural format
-   $ python neural_field_parser.py bracket_001
-   ├── Time: 15 seconds
-   ├── Output: neural_field_data.json (200 KB)
-   └──         neural_field_data.h5 (3.2 MB)
-
-4. Train neural network (once, on 500 brackets)
-   $ python train.py --train_dir ./brackets --epochs 150
-   ├── Time: 8 hours (one-time)
-   └── Output: checkpoint_best.pt (3 MB model)
-
-5. Predict new bracket design
-   $ python predict.py --model checkpoint_best.pt --input new_bracket
-   ├── Time: 15 milliseconds
-   ├── Output:
-   │   ├── Max displacement: 0.523 mm
-   │   ├── Max stress: 245.7 MPa
-   │   └── Complete stress field at all 15,432 nodes
-   └── Can now test 10,000 designs in 2.5 minutes!
-```
-
----
-
-## 🔧 How to Use Your System
-
-### Quick Reference Commands
-
-```bash
-# Navigate to project
-cd c:\Users\antoi\Documents\Atomaste\Atomizer-Field
-
-# Activate environment
-atomizer_env\Scripts\activate
-
-# ===== PHASE 1: Parse FEA Data =====
-
-# Single case
-python neural_field_parser.py case_001
-
-# Validate
-python validate_parsed_data.py case_001
-
-# Batch process
-python batch_parser.py ./all_cases
-
-# ===== PHASE 2: Train Neural Network =====
-
-# Train model
-python train.py \
-    --train_dir ./training_data \
-    --val_dir ./validation_data \
-    --epochs 100 \
-    --batch_size 4
-
-# Monitor training
-tensorboard --logdir runs/tensorboard
-
-# ===== PHASE 2: Run Predictions =====
-
-# Predict single case
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input test_case_001
-
-# Batch prediction
-python predict.py \
-    --model runs/checkpoint_best.pt \
-    --input ./test_cases \
-    --batch
-```
-
----
-
-## 📊 Expected Results
-
-### Phase 1 (Parser)
-
-**Input:**
-- BDF file: 1.2 MB
-- OP2 file: 4.5 MB
-
-**Output:**
-- JSON: ~200 KB (metadata)
-- HDF5: ~3 MB (fields)
-- Time: ~15 seconds
-
-### Phase 2 (Training)
-
-**Training Set:**
-- 500 parsed cases
-- Time: 8-12 hours
-- GPU: NVIDIA RTX 3080
-
-**Validation Accuracy:**
-- Displacement error: 3-5%
-- Stress error: 5-10%
-- Max value error: 1-3%
-
-### Phase 2 (Inference)
-
-**Per Prediction:**
-- Time: 5-50 milliseconds
-- Accuracy: Within 5% of FEA
-- Speedup: 10,000× - 500,000×
-
----
-
-## 🎓 What You Have Built
-
-You now have a complete system that:
-
-1. ✅ Parses NX Nastran results into ML-ready format
-2. ✅ Converts FEA meshes to graph neural network format
-3. ✅ Trains physics-informed GNNs to predict stress/displacement
-4. ✅ Runs inference 1000× faster than traditional FEA
-5. ✅ Provides complete field distributions (not just max values)
-6. ✅ Enables rapid design optimization
-
-**Total Implementation:**
-- ~3,000 lines of production-ready Python code
-- Comprehensive documentation
-- Complete testing framework
-- Ready for real optimization workflows
-
----
-
-This is a **revolutionary approach** to structural optimization that combines:
-- Traditional FEA accuracy
-- Neural network speed
-- Physics-informed learning
-- Graph-based topology understanding
-
-You're ready to transform hours of FEA into milliseconds of prediction! 🚀

atomizer-field/TESTING_CHECKLIST.md

@@ -1,277 +0,0 @@
-# AtomizerField Testing Checklist
-
-Quick reference for testing status and next steps.
-
----
-
-## ✅ Completed Tests
-
-### Environment Setup
-- [x] Conda environment created (`atomizer_field`)
-- [x] All dependencies installed
-- [x] NumPy MINGW-W64 issue resolved
-- [x] No segmentation faults
-
-### Smoke Tests (5/5)
-- [x] Model creation (128,589 parameters)
-- [x] Forward pass
-- [x] Loss functions (4 types)
-- [x] Batch processing
-- [x] Gradient flow
-
-### Simple Beam Test (7/7)
-- [x] File existence (BDF + OP2)
-- [x] Directory setup
-- [x] Module imports
-- [x] BDF/OP2 parsing (5,179 nodes, 4,866 elements)
-- [x] Data validation
-- [x] Graph conversion
-- [x] Neural prediction (95.94 ms)
-
-### Visualization
-- [x] 3D mesh rendering
-- [x] Displacement field (original + deformed)
-- [x] Stress field (von Mises)
-- [x] Report generation (markdown + images)
-
-### Unit Validation
-- [x] UNITSYS detection (MN-MM)
-- [x] Material properties (E = 200 GPa)
-- [x] Stress values (117 MPa reasonable)
-- [x] Force values (2.73 MN validated)
-- [x] Direction vectors preserved
-
----
-
-## ❌ Not Yet Tested (Requires Trained Model)
-
-### Physics Tests (0/4)
-- [ ] Cantilever beam (analytical comparison)
-- [ ] Equilibrium check (∇·σ + f = 0)
-- [ ] Constitutive law (σ = C:ε)
-- [ ] Energy conservation
-
-### Learning Tests (0/4)
-- [ ] Memorization (single case < 1% error)
-- [ ] Interpolation (between cases < 10% error)
-- [ ] Extrapolation (unseen loads < 20% error)
-- [ ] Pattern recognition (physics transfer)
-
-### Integration Tests (0/5)
-- [ ] Batch prediction
-- [ ] Gradient computation
-- [ ] Optimization loop
-- [ ] Uncertainty quantification
-- [ ] Online learning
-
-### Performance Tests (0/3)
-- [ ] Accuracy benchmark (< 10% error)
-- [ ] Speed benchmark (< 50 ms)
-- [ ] Scalability (10K+ nodes)
-
----
-
-## 🔧 Known Issues to Fix
-
-### Minor (Non-blocking)
-- [ ] Unit labels: "MPa" should be "kPa" (or convert values)
-- [ ] Missing SPCs warning (investigate BDF)
-- [ ] Unicode encoding (mostly fixed, minor cleanup remains)
-
-### Documentation
-- [ ] Unit conversion guide
-- [ ] Training data generation guide
-- [ ] User manual
-
----
-
-## 🚀 Testing Roadmap
-
-### Phase 1: Pre-Training Validation
-**Status:** ✅ COMPLETE
-
-- [x] Core pipeline working
-- [x] Test case validated
-- [x] Units understood
-- [x] Visualization working
-
-### Phase 2: Training Preparation
-**Status:** 🔜 NEXT
-
-- [ ] Fix unit labels (30 min)
-- [ ] Document unit system (1 hour)
-- [ ] Create training data generation script
-- [ ] Generate 50 test cases (1-2 weeks)
-
-### Phase 3: Initial Training
-**Status:** ⏸️ WAITING
-
-- [ ] Train on 50 cases (2-4 hours)
-- [ ] Validate on 10 held-out cases
-- [ ] Check loss convergence
-- [ ] Run memorization test
-
-### Phase 4: Physics Validation
-**Status:** ⏸️ WAITING
-
-- [ ] Cantilever beam test
-- [ ] Equilibrium check
-- [ ] Energy conservation
-- [ ] Compare vs analytical solutions
-
-### Phase 5: Full Validation
-**Status:** ⏸️ WAITING
-
-- [ ] Run full test suite (18 tests)
-- [ ] Accuracy benchmarks
-- [ ] Speed benchmarks
-- [ ] Scalability tests
-
-### Phase 6: Production Deployment
-**Status:** ⏸️ WAITING
-
-- [ ] Integration with Atomizer
-- [ ] End-to-end optimization test
-- [ ] Performance profiling
-- [ ] User acceptance testing
-
----
-
-## 📊 Test Commands Quick Reference
-
-### Run Tests
-```bash
-# Activate environment
-conda activate atomizer_field
-
-# Quick smoke tests (30 seconds)
-python test_suite.py --quick
-
-# Simple Beam end-to-end (1 minute)
-python test_simple_beam.py
-
-# Physics tests (15 minutes) - REQUIRES TRAINED MODEL
-python test_suite.py --physics
-
-# Full test suite (1 hour) - REQUIRES TRAINED MODEL
-python test_suite.py --full
-```
-
-### Visualization
-```bash
-# Mesh only
-python visualize_results.py test_case_beam --mesh
-
-# Displacement
-python visualize_results.py test_case_beam --displacement
-
-# Stress
-python visualize_results.py test_case_beam --stress
-
-# Full report
-python visualize_results.py test_case_beam --report
-```
-
-### Unit Validation
-```bash
-# Check parsed data units
-python check_units.py
-
-# Check OP2 raw data
-python check_op2_units.py
-
-# Check actual values
-python check_actual_values.py
-```
-
-### Training (When Ready)
-```bash
-# Generate training data
-python batch_parser.py --input Models/ --output training_data/
-
-# Train model
-python train.py \
-  --data_dirs training_data/* \
-  --epochs 100 \
-  --batch_size 16 \
-  --loss physics
-
-# Monitor training
-tensorboard --logdir runs/
-```
-
----
-
-## 📈 Success Criteria
-
-### Phase 1: Core System ✅
-- [x] All smoke tests passing
-- [x] End-to-end test passing
-- [x] Real FEA data processed
-- [x] Visualization working
-
-### Phase 2: Training Ready 🔜
-- [ ] Unit labels correct
-- [ ] 50+ training cases generated
-- [ ] Training script validated
-- [ ] Monitoring setup (TensorBoard)
-
-### Phase 3: Model Trained ⏸️
-- [ ] Training loss < 0.01
-- [ ] Validation loss < 0.05
-- [ ] No overfitting (train ≈ val loss)
-- [ ] Predictions physically reasonable
-
-### Phase 4: Physics Validated ⏸️
-- [ ] Equilibrium error < 1%
-- [ ] Constitutive error < 5%
-- [ ] Energy conservation < 5%
-- [ ] Analytical test < 5% error
-
-### Phase 5: Production Ready ⏸️
-- [ ] Prediction error < 10%
-- [ ] Inference time < 50 ms
-- [ ] All 18 tests passing
-- [ ] Integration with Atomizer working
-
----
-
-## 🎯 Current Focus
-
-**Status:** ✅ Core validation complete, ready for training phase
-
-**Next immediate steps:**
-1. Fix unit labels (optional, 30 min)
-2. Generate training data (critical, 1-2 weeks)
-3. Train model (critical, 2-4 hours)
-
-**Blockers:** None - system ready!
-
----
-
-## 📞 Quick Status Check
-
-Run this to verify system health:
-```bash
-conda activate atomizer_field
-python test_simple_beam.py
-```
-
-Expected output:
-```
-TEST 1: Files exist ✓
-TEST 2: Directory setup ✓
-TEST 3: Modules import ✓
-TEST 4: BDF/OP2 parsed ✓
-TEST 5: Data validated ✓
-TEST 6: Graph created ✓
-TEST 7: Prediction made ✓
-
-[SUCCESS] All 7 tests passed!
-```
-
----
-
-*Testing Checklist v1.0*
-*Last updated: November 24, 2025*
-*Status: Phase 1 complete, Phase 2 ready to start*

atomizer-field/TESTING_COMPLETE.md

@@ -1,673 +0,0 @@
-# AtomizerField Testing Framework - Complete Implementation
-
-## Overview
-
-The complete testing framework has been implemented for AtomizerField. All test modules are ready to validate the system from basic functionality through full neural FEA predictions.
-
----
-
-## Test Structure
-
-### Directory Layout
-```
-Atomizer-Field/
-├── test_suite.py                   # Master orchestrator
-├── test_simple_beam.py             # Specific test for Simple Beam model
-│
-├── tests/
-│   ├── __init__.py                 # Package initialization
-│   ├── test_synthetic.py           # Smoke tests (5 tests)
-│   ├── test_physics.py             # Physics validation (4 tests)
-│   ├── test_learning.py            # Learning capability (4 tests)
-│   ├── test_predictions.py         # Integration tests (5 tests)
-│   └── analytical_cases.py         # Analytical solutions library
-│
-└── test_results/                   # Auto-generated results
-```
-
----
-
-## Implemented Test Modules
-
-### 1. test_synthetic.py ✅ COMPLETE
-**Purpose:** Basic functionality validation (smoke tests)
-
-**5 Tests Implemented:**
-1. **Model Creation** - Verify GNN instantiates (718K params)
-2. **Forward Pass** - Model processes data correctly
-3. **Loss Computation** - All 4 loss types work (MSE, Relative, Physics, Max)
-4. **Batch Processing** - Handle multiple graphs
-5. **Gradient Flow** - Backpropagation works
-
-**Run standalone:**
-```bash
-python tests/test_synthetic.py
-```
-
-**Expected output:**
-```
-5/5 tests passed
-✓ Model creation successful
-✓ Forward pass works
-✓ Loss functions operational
-✓ Batch processing works
-✓ Gradients flow correctly
-```
-
----
-
-### 2. test_physics.py ✅ COMPLETE
-**Purpose:** Physics constraint validation
-
-**4 Tests Implemented:**
-1. **Cantilever Analytical** - Compare with δ = FL³/3EI
-   - Creates synthetic cantilever beam graph
-   - Computes analytical displacement
-   - Compares neural prediction
-   - Expected error: < 5% after training
-
-2. **Equilibrium Check** - Verify ∇·σ + f = 0
-   - Tests force balance
-   - Checks stress field consistency
-   - Expected residual: < 1e-6 after training
-
-3. **Energy Conservation** - Verify strain energy = work
-   - Computes external work (F·u)
-   - Computes strain energy (σ:ε)
-   - Expected balance: < 1% error
-
-4. **Constitutive Law** - Verify σ = C:ε
-   - Tests Hooke's law compliance
-   - Checks stress-strain proportionality
-   - Expected: Linear relationship
-
-**Run standalone:**
-```bash
-python tests/test_physics.py
-```
-
-**Note:** These tests will show physics compliance after model is trained with physics-informed losses.
-
----
-
-### 3. test_learning.py ✅ COMPLETE
-**Purpose:** Learning capability validation
-
-**4 Tests Implemented:**
-1. **Memorization Test** (10 samples, 100 epochs)
-   - Can network memorize small dataset?
-   - Expected: > 50% loss improvement
-   - Success criteria: Final loss < 0.1
-
-2. **Interpolation Test** (Train: [1,3,5,7,9], Test: [2,4,6,8])
-   - Can network generalize between training points?
-   - Expected: < 5% error after training
-   - Tests pattern recognition within range
-
-3. **Extrapolation Test** (Train: [1-5], Test: [7-10])
-   - Can network predict beyond training range?
-   - Expected: < 20% error (harder than interpolation)
-   - Tests robustness of learned patterns
-
-4. **Pattern Recognition** (Stiffness variation)
-   - Does network learn physics relationships?
-   - Expected: Stiffness ↑ → Displacement ↓
-   - Tests understanding vs memorization
-
-**Run standalone:**
-```bash
-python tests/test_learning.py
-```
-
-**Training details:**
-- Each test trains a fresh model
-- Uses synthetic datasets with known patterns
-- Demonstrates learning capability before real FEA training
-
----
-
-### 4. test_predictions.py ✅ COMPLETE
-**Purpose:** Integration tests for complete pipeline
-
-**5 Tests Implemented:**
-1. **Parser Validation**
-   - Checks test_case_beam directory exists
-   - Validates parsed JSON/HDF5 files
-   - Reports node/element counts
-   - Requires: Run `test_simple_beam.py` first
-
-2. **Training Pipeline**
-   - Creates synthetic dataset (5 samples)
-   - Trains model for 10 epochs
-   - Validates complete training loop
-   - Reports: Training time, final loss
-
-3. **Prediction Accuracy**
-   - Quick trains on test case
-   - Measures displacement/stress errors
-   - Reports inference time
-   - Expected: < 100ms inference
-
-4. **Performance Benchmark**
-   - Tests 4 mesh sizes: [10, 50, 100, 500] nodes
-   - Measures average inference time
-   - 10 runs per size for statistics
-   - Success: < 100ms for 100 nodes
-
-5. **Batch Inference**
-   - Processes 5 graphs simultaneously
-   - Reports batch processing time
-   - Tests optimization loop scenario
-   - Validates parallel processing capability
-
-**Run standalone:**
-```bash
-python tests/test_predictions.py
-```
-
----
-
-### 5. analytical_cases.py ✅ COMPLETE
-**Purpose:** Library of analytical solutions for validation
-
-**5 Analytical Cases:**
-
-1. **Cantilever Beam (Point Load)**
-   ```python
-   δ_max = FL³/3EI
-   σ_max = FL/Z
-   ```
-   - Full deflection curve
-   - Moment distribution
-   - Stress field
-
-2. **Simply Supported Beam (Center Load)**
-   ```python
-   δ_max = FL³/48EI
-   σ_max = FL/4Z
-   ```
-   - Symmetric deflection
-   - Support reactions
-   - Moment diagram
-
-3. **Axial Tension Bar**
-   ```python
-   δ = FL/EA
-   σ = F/A
-   ε = σ/E
-   ```
-   - Linear displacement
-   - Uniform stress
-   - Constant strain
-
-4. **Pressure Vessel (Thin-Walled)**
-   ```python
-   σ_hoop = pr/t
-   σ_axial = pr/2t
-   ```
-   - Hoop stress
-   - Axial stress
-   - Radial expansion
-
-5. **Circular Shaft Torsion**
-   ```python
-   θ = TL/GJ
-   τ_max = Tr/J
-   ```
-   - Twist angle
-   - Shear stress distribution
-   - Shear strain
-
-**Standard test cases:**
-- `get_standard_cantilever()` - 1m steel beam, 1kN load
-- `get_standard_simply_supported()` - 2m steel beam, 5kN load
-- `get_standard_tension_bar()` - 1m square bar, 10kN load
-
-**Run standalone to verify:**
-```bash
-python tests/analytical_cases.py
-```
-
-**Example output:**
-```
-1. Cantilever Beam (Point Load)
-  Max displacement: 1.905 mm
-  Max stress: 120.0 MPa
-
-2. Simply Supported Beam (Point Load at Center)
-  Max displacement: 0.476 mm
-  Max stress: 60.0 MPa
-  Reactions: 2500.0 N each
-
-...
-```
-
----
-
-## Master Test Orchestrator
-
-### test_suite.py ✅ COMPLETE
-
-**Four Testing Modes:**
-
-1. **Quick Mode** (`--quick`)
-   - Duration: ~5 minutes
-   - Tests: 5 smoke tests
-   - Purpose: Verify basic functionality
-   ```bash
-   python test_suite.py --quick
-   ```
-
-2. **Physics Mode** (`--physics`)
-   - Duration: ~15 minutes
-   - Tests: Smoke + Physics (9 tests)
-   - Purpose: Validate physics constraints
-   ```bash
-   python test_suite.py --physics
-   ```
-
-3. **Learning Mode** (`--learning`)
-   - Duration: ~30 minutes
-   - Tests: Smoke + Physics + Learning (13 tests)
-   - Purpose: Confirm learning capability
-   ```bash
-   python test_suite.py --learning
-   ```
-
-4. **Full Mode** (`--full`)
-   - Duration: ~1 hour
-   - Tests: All 18 tests
-   - Purpose: Complete validation
-   ```bash
-   python test_suite.py --full
-   ```
-
-**Features:**
-- Progress tracking
-- Detailed reporting
-- JSON results export
-- Clean pass/fail output
-- Duration tracking
-- Metrics collection
-
-**Output format:**
-```
-============================================================
-AtomizerField Test Suite v1.0
-Mode: QUICK
-============================================================
-
-[TEST] Model Creation
-  Description: Verify GNN model can be instantiated
-    Creating GNN model...
-    Model created: 718,221 parameters
-  Status: ✓ PASS
-  Duration: 0.15s
-
-...
-
-============================================================
-TEST SUMMARY
-============================================================
-
-Total Tests: 5
-  ✓ Passed: 5
-  ✗ Failed: 0
-  Pass Rate: 100.0%
-
-✓ ALL TESTS PASSED - SYSTEM READY!
-
-============================================================
-
-Total testing time: 0.5 minutes
-
-Results saved to: test_results/test_results_quick_1234567890.json
-```
-
----
-
-## Test for Simple Beam Model
-
-### test_simple_beam.py ✅ COMPLETE
-
-**Purpose:** Validate complete pipeline with user's actual Simple Beam model
-
-**7-Step Test:**
-1. Check Files - Verify beam_sim1-solution_1.dat and .op2 exist
-2. Setup Test Case - Create test_case_beam/ directory
-3. Import Modules - Verify pyNastran and AtomizerField imports
-4. Parse Beam - Parse BDF/OP2 files
-5. Validate Data - Run quality checks
-6. Load as Graph - Convert to PyG format
-7. Neural Prediction - Make prediction with model
-
-**Location of beam files:**
-```
-Models/Simple Beam/
-  ├── beam_sim1-solution_1.dat  (BDF)
-  └── beam_sim1-solution_1.op2  (Results)
-```
-
-**Run:**
-```bash
-python test_simple_beam.py
-```
-
-**Creates:**
-```
-test_case_beam/
-  ├── input/
-  │   └── model.bdf
-  ├── output/
-  │   └── model.op2
-  ├── neural_field_data.json
-  └── neural_field_data.h5
-```
-
----
-
-## Results Export
-
-### JSON Format
-
-All test runs save results to `test_results/`:
-
-```json
-{
-  "timestamp": "2025-01-24T12:00:00",
-  "mode": "quick",
-  "tests": [
-    {
-      "name": "Model Creation",
-      "description": "Verify GNN model can be instantiated",
-      "status": "PASS",
-      "duration": 0.15,
-      "message": "Model created successfully (718,221 params)",
-      "metrics": {
-        "parameters": 718221
-      }
-    },
-    ...
-  ],
-  "summary": {
-    "total": 5,
-    "passed": 5,
-    "failed": 0,
-    "pass_rate": 100.0
-  }
-}
-```
-
----
-
-## Testing Strategy
-
-### Progressive Validation
-
-```
-Level 1: Smoke Tests (5 min)
-    ↓
-  "Code runs, model works"
-    ↓
-Level 2: Physics Tests (15 min)
-    ↓
-  "Understands physics constraints"
-    ↓
-Level 3: Learning Tests (30 min)
-    ↓
-  "Can learn patterns"
-    ↓
-Level 4: Integration Tests (1 hour)
-    ↓
-  "Production ready"
-```
-
-### Development Workflow
-
-```
-1. Write code
-2. Run: python test_suite.py --quick (30s)
-3. If pass → Continue
-   If fail → Fix immediately
-4. Before commit: python test_suite.py --full (1h)
-5. All pass → Commit
-```
-
-### Training Validation
-
-```
-Before training:
-  - All smoke tests pass
-  - Physics tests show correct structure
-
-During training:
-  - Monitor loss curves
-  - Check physics residuals
-
-After training:
-  - All physics tests < 5% error
-  - Learning tests show convergence
-  - Integration tests < 10% prediction error
-```
-
----
-
-## Test Coverage
-
-### What's Tested
-
-✅ **Architecture:**
-- Model instantiation
-- Layer connectivity
-- Parameter counts
-- Forward pass
-
-✅ **Loss Functions:**
-- MSE loss
-- Relative loss
-- Physics-informed loss
-- Max error loss
-
-✅ **Data Pipeline:**
-- BDF/OP2 parsing
-- Graph construction
-- Feature engineering
-- Batch processing
-
-✅ **Physics Compliance:**
-- Equilibrium (∇·σ + f = 0)
-- Constitutive law (σ = C:ε)
-- Boundary conditions
-- Energy conservation
-
-✅ **Learning Capability:**
-- Memorization
-- Interpolation
-- Extrapolation
-- Pattern recognition
-
-✅ **Performance:**
-- Inference speed
-- Batch processing
-- Memory usage
-- Scalability
-
----
-
-## Running the Tests
-
-### Environment Setup
-
-**Note:** There is currently a NumPy compatibility issue on Windows with MINGW-W64 that causes segmentation faults. Tests are ready to run once this environment issue is resolved.
-
-**Options:**
-1. Use conda environment with proper NumPy build
-2. Use WSL (Windows Subsystem for Linux)
-3. Run on Linux system
-4. Wait for NumPy Windows compatibility fix
-
-### Quick Start (Once Environment Fixed)
-
-```bash
-# 1. Quick smoke test (30 seconds)
-python test_suite.py --quick
-
-# 2. Test with Simple Beam
-python test_simple_beam.py
-
-# 3. Physics validation
-python test_suite.py --physics
-
-# 4. Complete validation
-python test_suite.py --full
-```
-
-### Individual Test Modules
-
-```bash
-# Run specific test suites
-python tests/test_synthetic.py      # 5 smoke tests
-python tests/test_physics.py        # 4 physics tests
-python tests/test_learning.py       # 4 learning tests
-python tests/test_predictions.py    # 5 integration tests
-
-# Run analytical case examples
-python tests/analytical_cases.py    # See all analytical solutions
-```
-
----
-
-## Success Criteria
-
-### Minimum Viable Testing (Pre-Training)
-- ✅ All smoke tests pass
-- ✅ Physics tests run (may not pass without training)
-- ✅ Learning tests demonstrate convergence
-- ⏳ Simple Beam parses successfully
-
-### Production Ready (Post-Training)
-- ✅ All smoke tests pass
-- ⏳ Physics tests < 5% error
-- ⏳ Learning tests show interpolation < 5% error
-- ⏳ Integration tests < 10% prediction error
-- ⏳ Performance: 1000× speedup vs FEA
-
----
-
-## Implementation Status
-
-### Completed ✅
-1. Master test orchestrator (test_suite.py)
-2. Smoke tests (test_synthetic.py) - 5 tests
-3. Physics tests (test_physics.py) - 4 tests
-4. Learning tests (test_learning.py) - 4 tests
-5. Integration tests (test_predictions.py) - 5 tests
-6. Analytical solutions library (analytical_cases.py) - 5 cases
-7. Simple Beam test (test_simple_beam.py) - 7 steps
-8. Documentation and examples
-
-### Total Test Count: 18 tests + 7-step integration test
-
----
-
-## Next Steps
-
-### To Run Tests:
-1. **Resolve NumPy environment issue**
-   - Use conda: `conda install numpy`
-   - Or use WSL/Linux
-   - Or wait for Windows NumPy fix
-
-2. **Run smoke tests**
-   ```bash
-   python test_suite.py --quick
-   ```
-
-3. **Test with Simple Beam**
-   ```bash
-   python test_simple_beam.py
-   ```
-
-4. **Generate training data**
-   - Create multiple design variations
-   - Run FEA on each
-   - Parse all cases
-
-5. **Train model**
-   ```bash
-   python train.py --config training_config.yaml
-   ```
-
-6. **Validate trained model**
-   ```bash
-   python test_suite.py --full
-   ```
-
----
-
-## File Summary
-
-| File | Lines | Purpose | Status |
-|------|-------|---------|--------|
-| test_suite.py | 403 | Master orchestrator | ✅ Complete |
-| test_simple_beam.py | 377 | Simple Beam test | ✅ Complete |
-| tests/test_synthetic.py | 297 | Smoke tests | ✅ Complete |
-| tests/test_physics.py | 370 | Physics validation | ✅ Complete |
-| tests/test_learning.py | 410 | Learning tests | ✅ Complete |
-| tests/test_predictions.py | 400 | Integration tests | ✅ Complete |
-| tests/analytical_cases.py | 450 | Analytical library | ✅ Complete |
-
-**Total:** ~2,700 lines of comprehensive testing infrastructure
-
----
-
-## Testing Philosophy
-
-### Fast Feedback
-- Smoke tests in 30 seconds
-- Catch errors immediately
-- Continuous validation during development
-
-### Comprehensive Coverage
-- From basic functionality to full pipeline
-- Physics compliance verification
-- Learning capability confirmation
-- Performance benchmarking
-
-### Progressive Confidence
-```
-Code runs → Understands physics → Learns patterns → Production ready
-```
-
-### Automated Validation
-- JSON results export
-- Clear pass/fail reporting
-- Metrics tracking
-- Duration monitoring
-
----
-
-## Conclusion
-
-**The complete testing framework is implemented and ready for use.**
-
-**What's Ready:**
-- 18 comprehensive tests across 4 test suites
-- Analytical solutions library with 5 classical cases
-- Master orchestrator with 4 testing modes
-- Simple Beam integration test
-- Detailed documentation and examples
-
-**To Use:**
-1. Resolve NumPy environment issue
-2. Run: `python test_suite.py --quick`
-3. Validate: All smoke tests should pass
-4. Proceed with training and full validation
-
-**The testing framework provides complete validation from zero to production-ready neural FEA predictions!** ✅
-
----
-
-*AtomizerField Testing Framework v1.0 - Complete Implementation*
-*Total: 18 tests + analytical library + integration test*
-*Ready for immediate use once environment is configured*