- 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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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.
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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
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- 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
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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
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- 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>
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
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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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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)
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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
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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)
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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>
Phase 2 - Structural Analysis:
- extract_principal_stress: σ1, σ2, σ3 principal stresses from OP2
- extract_strain_energy: Element and total strain energy
- extract_spc_forces: Reaction forces at boundary conditions
Phase 3 - Multi-Physics:
- extract_temperature: Nodal temperatures from thermal OP2 (SOL 153/159)
- extract_temperature_gradient: Thermal gradient approximation
- extract_heat_flux: Element heat flux from thermal analysis
- extract_modal_mass: Modal effective mass from F06 (SOL 103)
- get_first_frequency: Convenience function for first natural frequency
Documentation:
- Updated SYS_12_EXTRACTOR_LIBRARY.md with E12-E18 specifications
- Updated NX_OPEN_AUTOMATION_ROADMAP.md marking Phase 3 complete
- Added test_phase3_extractors.py for validation
All extractors follow consistent API pattern returning Dict with
success, data, and error fields for robust error handling.
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Dashboard enhancements:
- Add Analysis page with tabs: Overview, Parameters, Pareto, Correlations, Constraints, Surrogate, Runs
- Add PlotlyCorrelationHeatmap for parameter-objective correlation analysis
- Add PlotlyFeasibilityChart for constraint satisfaction visualization
- Add PlotlySurrogateQuality for FEA vs NN prediction comparison
- Add PlotlyRunComparison for comparing optimization runs within a study
Real-time improvements:
- Replace watchdog file-watching with SQLite database polling for better Windows reliability
- Add DatabasePoller class with 2-second polling interval
- Enhanced WebSocket messages: trial_completed, new_best, pareto_update, progress
Desktop notifications:
- Add useNotifications hook using Web Notifications API
- Add NotificationSettings toggle component
- Notify users when new best solutions are found
Config editor:
- Add PUT /studies/{study_id}/config endpoint with auto-backup
- Add ConfigEditor modal with tabs: General, Variables, Objectives, Settings, JSON
- Prevents editing while optimization is running
Enhanced Pareto visualization:
- Add dark mode styling with transparent backgrounds
- Add stats bar showing Pareto, FEA, NN, and infeasible counts
- Add Pareto front connecting line for 2D view
- Add table showing top 10 Pareto-optimal solutions
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add embedded Claude Code terminal with xterm.js for full CLI experience
- Create WebSocket PTY backend for real-time terminal communication
- Add terminal status endpoint to check CLI availability
- Update dashboard to use Claude Code terminal instead of API chat
- Add optimization control panel with start/stop/validate actions
- Add study context provider for global state management
- Update frontend with new dependencies (xterm.js addons)
- Comprehensive README documentation for all new features
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add ConvergencePlot component with running best, statistics, gradient fill
- Add ParameterImportanceChart with Pearson correlation analysis
- Add StudyReportViewer with KaTeX math rendering and full markdown support
- Update pruning endpoint to query Optuna database directly
- Add /report endpoint for STUDY_REPORT.md files
- Fix chart data transformation for single/multi-objective studies
- Update Protocol 13 documentation with new components
- Update generate-report skill with dashboard integration
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
When running optimization studies with multiple solutions (e.g., static + modal),
NX opens solution monitor windows for each trial. These windows superpose and cause
usability issues during long optimization runs.
Solution:
- Automatically disable solution monitor when solving all solutions (solution_name=None)
- Loop through all solutions and set "solution monitor" property to False
- Implemented in solve_simulation.py before solve execution (lines 271-295)
- Includes error handling with graceful fallback
Benefits:
- No monitor window pile-up during optimization studies
- Better performance (no GUI overhead)
- No user configuration required - works automatically
- Based on user-recorded journal (journal_monitor_window_off.py)
Documentation:
- Updated docs/NX_MULTI_SOLUTION_PROTOCOL.md with solution monitor control section
- Added implementation details and when the feature activates
- Cross-referenced user's recorded journal
Implementation: optimization_engine/solve_simulation.py
Documentation: docs/NX_MULTI_SOLUTION_PROTOCOL.md
Reference: nx_journals/user_generated_journals/journal_monitor_window_off.py
Migrate drone_gimbal_arm study as reference implementation for Phase 1.3 logging system.
Changes:
- Replace all print() statements with logger calls throughout run_optimization.py
- Add logger.trial_start() and logger.trial_complete() for structured trial logging
- Use logger.trial_failed() for error handling with full tracebacks
- Add logger.study_start() and logger.study_complete() for lifecycle logging
- Replace constraint violation prints with logger.warning()
- Create comprehensive LOGGING_MIGRATION_GUIDE.md with before/after examples
Benefits:
- Color-coded console output (green INFO, yellow WARNING, red ERROR)
- Automatic file logging to 2_results/optimization.log with rotation (50MB, 3 backups)
- Structured format with timestamps for dashboard integration
- Professional error handling with exc_info=True
- Reference implementation for migrating remaining studies
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements JSON Schema validation for optimization configurations to ensure
consistency across all studies and prevent configuration errors.
Added:
- optimization_engine/schemas/optimization_config_schema.json
- Comprehensive schema for Protocol 10 & 11 configurations
- Validates objectives, constraints, design variables, simulation settings
- Enforces standard field names (goal, bounds, parameter, threshold)
- optimization_engine/config_manager.py
- ConfigManager class with schema validation
- CLI tool: python config_manager.py <config.json>
- Type-safe accessor methods for config elements
- Custom validations: bounds check, multi-objective consistency, location check
- optimization_engine/schemas/README.md
- Complete documentation of standard configuration format
- Validation examples and common error fixes
- Migration guidance for legacy configs
- docs/07_DEVELOPMENT/Phase_1_2_Implementation_Plan.md
- Detailed implementation plan for remaining Phase 1.2 tasks
- Migration tool design, integration guide, testing plan
Testing:
- Validated drone_gimbal_arm_optimization config successfully
- ConfigManager works with drone_gimbal format (new standard)
- Identifies legacy format issues in bracket studies
Standards Established:
- Configuration location: studies/{name}/1_setup/
- Objective direction: "goal" not "type"
- Design var bounds: "bounds": [min, max] not "min"/"max"
- Design var name: "parameter" not "name"
- Constraint threshold: "threshold" not "value"
Next Steps (Phase 1.2.1+):
- Config migration tool for legacy studies
- Integration with run_optimization.py
- Update create-study Claude skill with schema reference
- Migrate bracket studies to new format
Relates to: Phase 1.2 MVP Development Plan
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
MAJOR ARCHITECTURE REFACTOR - Clean Study Folders
Problem Identified by User:
"My study folder is a mess, why? I want some order and real structure to develop
an insanely good engineering software that evolve with time."
- Every substudy was generating duplicate extractor code
- Study folders polluted with reusable library code (generated_extractors/, generated_hooks/)
- No code reuse across studies
- Not production-grade architecture
Solution - Centralized Library System:
Implemented smart library with signature-based deduplication:
- Core extractors in optimization_engine/extractors/
- Studies only store metadata (extractors_manifest.json)
- Clean separation: studies = data, core = code
Architecture:
BEFORE (BAD):
studies/my_study/
generated_extractors/ ❌ Code pollution!
extract_displacement.py
extract_von_mises_stress.py
generated_hooks/ ❌ Code pollution!
llm_workflow_config.json
results.json
AFTER (GOOD):
optimization_engine/extractors/ ✓ Core library
extract_displacement.py
extract_stress.py
catalog.json
studies/my_study/
extractors_manifest.json ✓ Just references!
llm_workflow_config.json ✓ Config
optimization_results.json ✓ Results
New Components:
1. ExtractorLibrary (extractor_library.py)
- Signature-based deduplication
- Centralized catalog (catalog.json)
- Study manifest generation
- Reusability across all studies
2. Updated ExtractorOrchestrator
- Uses core library instead of per-study generation
- Creates manifest instead of copying code
- Backward compatible (legacy mode available)
3. Updated LLMOptimizationRunner
- Removed generated_extractors/ directory creation
- Removed generated_hooks/ directory creation
- Uses core library exclusively
4. Updated Tests
- Verifies extractors_manifest.json exists
- Checks for clean study folder structure
- All 18/18 checks pass
Results:
Study folders NOW ONLY contain:
✓ extractors_manifest.json - references to core library
✓ llm_workflow_config.json - study configuration
✓ optimization_results.json - optimization results
✓ optimization_history.json - trial history
✓ .db file - Optuna database
Core library contains:
✓ extract_displacement.py - reusable across ALL studies
✓ extract_von_mises_stress.py - reusable across ALL studies
✓ extract_mass.py - reusable across ALL studies
✓ catalog.json - tracks all extractors with signatures
Benefits:
- Clean, professional study folder structure
- Code reuse eliminates duplication
- Library grows over time, studies stay clean
- Production-grade architecture
- "Insanely good engineering software that evolves with time"
Testing:
E2E test passes with clean folder structure
- No generated_extractors/ pollution
- Manifest correctly references library
- Core library populated with reusable extractors
- Study folder professional and minimal
Documentation:
- Added comprehensive architecture doc (docs/ARCHITECTURE_REFACTOR_NOV17.md)
- Includes migration guide
- Documents future work (hooks library, versioning, CLI tools)
Next Steps:
- Apply same architecture to hooks library
- Add auto-generated documentation for library
- Implement versioning for reproducibility
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Co-Authored-By: Claude <noreply@anthropic.com>
Created comprehensive roadmap for remaining Phase 3.2 work:
Week 1 Summary (COMPLETE):
- Task 1.2: LLMOptimizationRunner wired to production
- Task 1.3: Minimal example created
- All tests passing, documentation updated
Immediate Next Steps:
- Task 1.4: End-to-end integration test (2-4 hours)
Week 2 Plan - Robustness & Safety (16 hours):
- Code validation system (syntax, security, schema)
- Fallback mechanisms for all failure modes
- Comprehensive test suite (>80% coverage)
- Audit trail for generated code
Week 3 Plan - Learning System (20 hours):
- Template library with validated code patterns
- Knowledge base integration
- Success metrics and learning from patterns
Week 4 Plan - Documentation (12 hours):
- User guide for LLM mode
- Architecture documentation
- Demo video and presentation
Success Criteria:
- Production-ready LLM mode with safety validation
- Fallback mechanisms for robustness
- Learning system that improves over time
- Complete documentation for users
Known Gaps:
1. LLMWorkflowAnalyzer Claude Code integration (Phase 2.7)
2. Manual mode integration (lower priority)
Recommendations:
1. Complete Task 1.4 E2E test this week
2. Use API key for testing (don't block on Claude Code)
3. Prioritize safety (Week 2) before features
4. Build template library early (Week 3)
Overall Progress: 25% complete (1 week / 4 weeks)
Timeline: ON TRACK
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Major Features Added:
1. Centralized Configuration System (config.py)
- Single source of truth for all NX and environment paths
- Change NX version in ONE place: NX_VERSION = "2412"
- Change Python environment in ONE place: PYTHON_ENV_NAME = "atomizer"
- Automatic path derivation and validation
- Helper functions: get_nx_journal_command()
- Future-proof: Easy to upgrade when NX 2506+ released
2. NX Path Corrections (Critical Fix)
- Fixed all incorrect Simcenter3D_2412 references to NX2412
- Updated nx_updater.py to use config.NX_RUN_JOURNAL
- Updated dashboard/api/app.py to use config.NX_RUN_JOURNAL
- Corrected material library path to NX2412/UGII/materials
- All files now use correct NX2412 installation
3. NX Expression Import System
- Dual-method expression gathering (.exp export + binary parsing)
- Robust handling of all NX expression types
- Support for formulas, units, and dependencies
- Documented in docs/NX_EXPRESSION_IMPORT_SYSTEM.md
4. Study Management & Analysis Tools
- StudyCreator: Unified interface for study/substudy creation
- BenchmarkingSubstudy: Automated baseline analysis
- ComprehensiveResultsAnalyzer: Multi-result extraction from .op2
- Expression extractor generator (LLM-powered)
5. 50-Trial Beam Optimization Complete
- Full optimization results documented
- Best design: 23.1% improvement over baseline
- Comprehensive analysis with plots and insights
- Results in studies/simple_beam_optimization/
Documentation Updates:
- docs/SYSTEM_CONFIGURATION.md - System paths and validation
- docs/QUICK_CONFIG_REFERENCE.md - Quick config change guide
- docs/NX_EXPRESSION_IMPORT_SYSTEM.md - Expression import details
- docs/OPTIMIZATION_WORKFLOW.md - Complete workflow guide
- Updated README.md with NX2412 paths
Files Modified:
- config.py (NEW) - Central configuration system
- optimization_engine/nx_updater.py - Now uses config
- dashboard/api/app.py - Now uses config
- optimization_engine/study_creator.py - Enhanced features
- optimization_engine/benchmarking_substudy.py - New analyzer
- optimization_engine/comprehensive_results_analyzer.py - Multi-result extraction
- optimization_engine/result_extractors/generated/extract_expression.py - Generated extractor
Cleanup:
- Removed all temporary test files
- Removed migration scripts (no longer needed)
- Clean production-ready codebase
Strategic Impact:
- Configuration maintenance time: reduced from hours to seconds
- Path consistency: 100% enforced across codebase
- Future NX upgrades: Edit ONE variable in config.py
- Foundation for Phase 3.2 Integration completion
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