Canvas Builder Visual Updates:
- Update all Canvas components to use Atomaster dark theme
- BaseNode: dark background (bg-dark-800), white text, primary selection glow
- NodePalette: dark sidebar with hover states
- NodeConfigPanel: dark inputs, labels, and panel background
- ValidationPanel: semi-transparent error/warning panels with backdrop blur
- ChatPanel: dark message area with themed welcome state
- ExecuteDialog: dark modal with primary button styling
- ConfigImporter: dark tabs, inputs, and file upload zone
- TemplateSelector: dark cards with category pills and hover effects
Setup Page Integration:
- Add Configuration/Canvas Builder tab switcher
- Canvas tab renders AtomizerCanvas full-height
- Tabs styled to match Atomaster theme
Build: Passes TypeScript and Vite build
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add canvas.ts MCP tool with validate_canvas_intent, execute_canvas_intent, interpret_canvas_intent
- Add useCanvasChat.ts bridge hook connecting canvas to chat system
- Update context_builder.py with canvas tool instructions
- Add ExecuteDialog for study name input
- Add ChatPanel for canvas-integrated Claude responses
- Connect AtomizerCanvas to Claude via useCanvasChat
Canvas workflow now:
1. Build graph visually
2. Click Validate/Analyze/Execute
3. Claude processes intent via MCP tools
4. Response shown in integrated chat panel
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Major changes:
- Dashboard: WebSocket-based chat with session management
- Dashboard: New chat components (ChatPane, ChatInput, ModeToggle)
- Dashboard: Enhanced UI with parallel coordinates chart
- MCP Server: New atomizer-tools server for Claude integration
- Extractors: Enhanced Zernike OPD extractor
- Reports: Improved report generator
New studies (configs and scripts only):
- M1 Mirror: Cost reduction campaign studies
- Simple Beam, Simple Bracket, UAV Arm studies
Note: Large iteration data (2_iterations/, best_design_archive/)
excluded via .gitignore - kept on local Gitea only.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update README.md with LLM assistant section
- Create optimization_memory JSONL structure
- Move implementation plans from skills/modules to docs/plans
- Verify all imports work correctly
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add neural optional group (torch, torch-geometric, tensorboard)
- Add gnn optional group (torch, torch-geometric)
- Add all optional group for convenience
- Remove mcp optional group (not implemented)
- Remove mcp_server from packages.find
- Update pytest coverage config
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Consolidate surrogates module to processors/surrogates/
- Move ensemble_surrogate.py to proper location
- Add deprecation shim for old import path
- Create tests/ directory with pytest structure
- Move test files from archive/test_scripts/
- Add conftest.py with shared fixtures
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- Delete orphaned files: temp_compare.py, run_cleanup.py
- Delete stale cache files from archive/temp_outputs/
- Update .gitignore with .coverage.*, .obsidian/ entries
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Study Interview Mode is now the DEFAULT for all study creation requests.
This intelligent Q&A system guides users through optimization setup with:
- 7-phase interview flow: introspection → objectives → constraints → design_variables → validation → review → complete
- Material-aware validation with 12 materials and fuzzy name matching
- Anti-pattern detection for 12 common mistakes (mass-no-constraint, stress-over-yield, etc.)
- Auto extractor mapping E1-E24 based on goal keywords
- State persistence with JSON serialization and backup rotation
- StudyBlueprint generation with full validation
Triggers: "create a study", "new study", "optimize this", any study creation intent
Skip with: "skip interview", "quick setup", "manual config"
Components:
- StudyInterviewEngine: Main orchestrator
- QuestionEngine: Conditional logic evaluation
- EngineeringValidator: MaterialsDatabase + AntiPatternDetector
- InterviewPresenter: Markdown formatting for Claude
- StudyBlueprint: Validated configuration output
- InterviewState: Persistent state management
All 129 tests passing.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Self-Aware Turbo v3 optimization validated on M1 Mirror flat back:
- Best WS: 205.58 (12% better than previous best 218.26)
- 100% feasibility rate, 100% unique designs
- Uses 556 training samples from V5-V8 campaign data
Key innovations in V9:
- Adaptive exploration schedule (15% → 8% → 3%)
- Mass threshold at 118 kg (optimal sweet spot)
- 70% exploitation near best design
- Seeded with best known design from V7
- Ensemble surrogate with R²=0.99
Updated documentation:
- SYS_16: SAT protocol updated to v3.0 VALIDATED
- Cheatsheet: Added SAT v3 as recommended method
- Context: Updated protocol overview
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added new PART 8: Self-Aware Turbo (SAT) - Validated Breakthrough
- Explains ensemble surrogate with epistemic uncertainty
- Documents OOD detection and adaptive exploration schedule
- Includes V9 results: WS=205.58 (best ever)
- Added SAT sound bites for podcast
- Updated document to 12 sections
Generated with Claude Code
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Key changes based on feedback:
- Reposition as "optimizer & NX configurator" not "LLM-first"
- Add Part 2: Study Characterization & Performance Learning
- Add Part 3: Protocol Evolution workflow (Research → Review → Approve)
- Add Part 4: MCP-first development approach with documentation hierarchy
- Emphasize simulation optimization over CAD/mesh concerns
- Add LAC knowledge accumulation for parameter-performance relationships
- Add privilege levels for protocol approval (user/power_user/admin)
- Update sound bites and core messaging
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Added GitHub remote URL (Anto01/Atomizer) to CLAUDE.md so it persists
across sessions. Also recorded in LAC user_preference.jsonl.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Expanded Key Directories section with full optimization_engine structure
- Added Import Migration section with new import paths
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update feature_registry.json paths to new module locations (v0.3.0)
- Update cheatsheet with new import paths (v2.3)
- Mark migration plan as completed (v3.0)
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add TrialManager (trial_manager.py) for consistent trial_NNNN naming
- Add DashboardDB (dashboard_db.py) for Optuna-compatible database schema
- Update CLAUDE.md with trial management documentation
- Update ATOMIZER_CONTEXT.md with v1.8 trial system
- Update cheatsheet v2.2 with new utilities
- Update SYS_14 protocol to v2.3 with TrialManager integration
- Add LAC learnings for trial management patterns
- Add archive/README.md for deprecated code policy
Key principles:
- Trial numbers NEVER reset (monotonic)
- Folders NEVER get overwritten
- Database always synced with filesystem
- Surrogate predictions are NOT trials (only FEA results)
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Adds tests/debug_lateral_discrepancy.py to investigate differences between
Zernike OPD lateral displacement reporting and Simcenter post-processing.
Key findings documented:
- OPD reports sqrt(dx² + dy²) - combined XY magnitude
- Simcenter shows individual components (dx or dy)
- Both are correct, OPD magnitude is more meaningful for optics
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Replace Standard (Z-only) extraction with OPD method (X,Y,Z displacement)
- Add toggle buttons to switch between WFE, ΔX, ΔY, ΔZ views
- Show method comparison metrics (OPD vs Standard RMS difference)
- Display lateral displacement statistics (max/RMS)
- Fall back to Standard method if BDF geometry file not found
- Use ZernikeOPDExtractor for more accurate WFE computation (+8-11%)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Major improvements to Zernike WFE visualization:
- Add ZernikeDashboardInsight: Unified dashboard with all orientations (40°, 60°, 90°)
on one page with light theme and executive summary
- Add OPD method toggle: Switch between Standard (Z-only) and OPD (X,Y,Z) methods
in ZernikeWFEInsight with interactive buttons
- Add lateral displacement maps: Visualize X,Y displacement for each orientation
- Add displacement component views: Toggle between WFE, ΔX, ΔY, ΔZ in relative views
- Add metrics comparison table showing both methods side-by-side
New extractors:
- extract_zernike_figure.py: ZernikeOPDExtractor using BDF geometry interpolation
- extract_zernike_opd.py: Parabola-based OPD with focal length
Key finding: OPD method gives 8-11% higher WFE values than Standard method
(more conservative/accurate for surfaces with lateral displacement under gravity)
Documentation updates:
- SYS_12: Added E22 ZernikeOPD as recommended method
- SYS_16: Added ZernikeDashboard, updated ZernikeWFE with OPD features
- Cheatsheet: Added Zernike method comparison table
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Dashboard integration for Study Insights module (SYS_16):
- Backend: New /api/insights/ routes for generating and viewing insights
- Frontend: New Insights.tsx page with Plotly visualization
- Navigation: Added Insights tab between Analysis and Results
Available insight types:
- Zernike WFE (wavefront error for mirrors)
- Stress Field (Von Mises stress contours)
- Modal Analysis (natural frequencies/mode shapes)
- Thermal Field (temperature distribution)
- Design Space (parameter-objective exploration)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Remove old study folders that have been superseded or archived:
- bracket_pareto_3obj
- bracket_stiffness_optimization (V1-V3)
- bracket_stiffness_optimization_atomizerfield
- drone_gimbal_arm_optimization
- m1_mirror_adaptive_V11 through V15
- m1_mirror_zernike_optimization
- simple_beam_optimization
- training_data_export_test
- uav_arm_atomizerfield_test
These studies have been consolidated or are no longer needed.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New Tools (tools/):
- analyze_study.py: Generate comprehensive optimization reports
- find_best_iteration.py: Find best iteration folder, optionally copy it
- archive_best_design.py: Archive best design to 3_results/best_design_archive/<timestamp>/
Protocol Updates:
- OP_02_RUN_OPTIMIZATION.md v1.1: Add mandatory archive_best_design step
in Post-Run Actions. This MUST be done after every optimization run.
V14 Updates:
- run_optimization.py: Auto-archive best design at end of optimization
- optimization_config.json: Expand bounds for V14 continuation
- lateral_outer_angle: min 13->11 deg (was at 4.7%)
- lateral_inner_pivot: min 7->5 mm (was at 8.1%)
- lateral_middle_pivot: max 23->27 mm (was at 99.4%)
- whiffle_min: max 60->72 mm (was at 96.3%)
Usage:
python tools/analyze_study.py m1_mirror_adaptive_V14
python tools/find_best_iteration.py m1_mirror_adaptive_V14
python tools/archive_best_design.py m1_mirror_adaptive_V14
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New E11 Part Mass Extractor:
- Add nx_journals/extract_part_mass_material.py - NX journal using
NXOpen.MeasureManager.NewMassProperties() for accurate geometry-based mass
- Add optimization_engine/extractors/extract_part_mass_material.py - Python
wrapper that reads JSON output from journal
- Add E11 entry to extractors/catalog.json
Documentation Updates:
- SYS_12_EXTRACTOR_LIBRARY.md: Add mass accuracy warning noting pyNastran
get_mass_breakdown() under-reports ~7% on hex-dominant meshes with
tet/pyramid fill elements. E11 (geometry .prt) should be preferred over
E4 (BDF) unless material is overridden at FEM level.
- 01_CHEATSHEET.md: Add mass extraction tip
V14 Config:
- Expand design variable bounds (blank_backface_angle max 4.5°,
whiffle_triangle_closeness max 80mm, whiffle_min max 60mm)
Testing showed:
- E11 from .prt: 97.66 kg (accurate - matches NX GUI)
- E4 pyNastran get_mass_breakdown(): 90.73 kg (~7% under-reported)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Comprehensive guide teaching users how to interact with Atomizer so that
the learning system evolves correctly. Covers:
- The right mindset (colleague, not tool)
- Starting sessions with proper context
- Communicating goals, constraints, preferences
- Creating and running optimization studies
- Analyzing and validating results
- Reporting errors effectively
- Contributing to LAC (recording insights, outcomes, workarounds)
- Ending sessions properly to capture learnings
Includes:
- Mermaid diagrams for learning loop and flows
- Good vs bad examples for every interaction type
- Complete example session transcript
- Quick reference card for common patterns
- Golden rules summary
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.
## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions
## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern
## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Analysis of 258 FEA trials showed best trial (#45) hitting bounds on
5 parameters. Expanded bounds to allow exploration of promising regions:
- lateral_inner_angle: max 28.5 → 30.0° (was at 99.2% of range)
- lateral_inner_pivot: min 9.0 → 7.0 mm (was at 4.6% of range)
- lateral_middle_pivot: min 18.0 → 15.0 mm (was at 7.7% of range)
- whiffle_min: min 35.0 → 30.0 mm (was at 4.0% of range)
- whiffle_outer_to_vertical: min 68.0 → 60.0° (was at 5.3% of range)
- blank_backface_angle: narrowed to 4.1-4.2° (focus on optimal region)
V14 seeds from 496 prior FEA trials (V11+V12+V13) using TPE sampler.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The Method Selector now uses relative accuracy thresholds to assess
NN suitability by comparing NN error to problem variability (CV ratio).
NNQualityAssessor features:
- Physics-based objective classification (linear, smooth, nonlinear, chaotic)
- CV ratio computation: nn_error / coefficient_of_variation
- Turbo suitability score based on relative thresholds
- Data collection from validation_report.json, turbo_report.json, and study.db
Quality thresholds by objective type:
- Linear (mass, volume): max 2% error, CV ratio < 0.5
- Smooth (frequency): max 5% error, CV ratio < 1.0
- Nonlinear (stress, stiffness): max 10% error, CV ratio < 2.0
- Chaotic (contact, buckling): max 20% error, CV ratio < 3.0
CLI output now includes:
- Per-objective NN quality table with error, CV, ratio, and quality indicator
- Turbo suitability and hybrid suitability percentages
- Warnings when NN error exceeds physics-based thresholds
Updated SYS_15_METHOD_SELECTOR.md to v2.0 with full NN Quality Assessment documentation.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
