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1612991d0dd32bcb3f8f0c8751e812a4c9462da7
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6 Commits

Author SHA1 Message Date
Anto01
1612991d0d feat: Add Study Insights module (SYS_16) for physics visualizations 
Introduces a new plugin architecture for study-specific physics
visualizations, separating "optimizer perspective" (Analysis) from
"engineer perspective" (Insights).

New module: optimization_engine/insights/
- base.py: StudyInsight base class, InsightConfig, InsightResult, registry
- zernike_wfe.py: Mirror WFE with 3D surface and Zernike decomposition
- stress_field.py: Von Mises stress contours with safety factors
- modal_analysis.py: Natural frequencies and mode shapes
- thermal_field.py: Temperature distribution visualization
- design_space.py: Parameter-objective landscape exploration

Features:
- 5 insight types: zernike_wfe, stress_field, modal, thermal, design_space
- CLI: python -m optimization_engine.insights generate <study>
- Standalone HTML generation with Plotly
- Enhanced Zernike viz: Turbo colorscale, smooth shading, 0.5x AMP
- Dashboard API fix: Added include_coefficients param to extract_relative()

Documentation:
- docs/protocols/system/SYS_16_STUDY_INSIGHTS.md
- Updated ATOMIZER_CONTEXT.md (v1.7)
- Updated 01_CHEATSHEET.md with insights section

Tools:
- tools/zernike_html_generator.py: Standalone WFE HTML generator
- tools/analyze_wfe.bat: Double-click to analyze OP2 files

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-20 13:46:28 -05:00
Antoine
01a7d7d121 docs: Complete M1 mirror optimization campaign V11-V15 
## M1 Mirror Campaign Summary
- V11-V15 optimization campaign completed (~1,400 FEA evaluations)
- Best design: V14 Trial #725 with Weighted Sum = 121.72
- V15 NSGA-II confirmed V14 TPE found optimal solution
- Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9%

## Key Results
- 40° tracking: 5.99 nm (target 4.0 nm)
- 60° tracking: 13.10 nm (target 10.0 nm)
- Manufacturing: 26.28 nm (target 20.0 nm)
- Targets not achievable within current design space

## Documentation Added
- V15 STUDY_REPORT.md: Detailed NSGA-II results analysis
- M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview
- Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns
- Updated 01_CHEATSHEET.md with --resume guidance
- Updated OP_01_CREATE_STUDY.md with FEARunner template

## Studies Added
- m1_mirror_adaptive_V13: TPE validation (291 trials)
- m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST)
- m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-16 14:55:23 -05:00
Antoine
70ac34e3d3 feat: Add E11 Part Mass extractor, document pyNastran mass accuracy issue 
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>
 2025-12-11 22:15:36 -05:00
Antoine
fc123326e5 feat: Integrate Learning Atomizer Core (LAC) and master instructions 
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>
 2025-12-11 21:55:01 -05:00
Antoine
0e04457539 feat: Implement Agentic Architecture for robust session workflows 
Phase 1 - Session Bootstrap:
- Add .claude/ATOMIZER_CONTEXT.md as single entry point for new sessions
- Add study state detection and task routing

Phase 2 - Code Deduplication:
- Add optimization_engine/base_runner.py (ConfigDrivenRunner)
- Add optimization_engine/generic_surrogate.py (ConfigDrivenSurrogate)
- Add optimization_engine/study_state.py for study detection
- Add optimization_engine/templates/ with registry and templates
- Studies now require ~50 lines instead of ~300

Phase 3 - Skill Consolidation:
- Add YAML frontmatter metadata to all skills (versioning, dependencies)
- Consolidate create-study.md into core/study-creation-core.md
- Update 00_BOOTSTRAP.md, 01_CHEATSHEET.md, 02_CONTEXT_LOADER.md

Phase 4 - Self-Expanding Knowledge:
- Add optimization_engine/auto_doc.py for auto-generating documentation
- Generate docs/generated/EXTRACTORS.md (27 extractors documented)
- Generate docs/generated/TEMPLATES.md (6 templates)
- Generate docs/generated/EXTRACTOR_CHEATSHEET.md

Phase 5 - Subagent Implementation:
- Add .claude/commands/study-builder.md (create studies)
- Add .claude/commands/nx-expert.md (NX Open API)
- Add .claude/commands/protocol-auditor.md (config validation)
- Add .claude/commands/results-analyzer.md (results analysis)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-07 14:52:25 -05:00
Antoine
602560c46a feat: Add MLP surrogate with Turbo Mode for 100x faster optimization 
Neural Acceleration (MLP Surrogate):
- Add run_nn_optimization.py with hybrid FEA/NN workflow
- MLP architecture: 4-layer (64->128->128->64) with BatchNorm/Dropout
- Three workflow modes:
  - --all: Sequential export->train->optimize->validate
  - --hybrid-loop: Iterative Train->NN->Validate->Retrain cycle
  - --turbo: Aggressive single-best validation (RECOMMENDED)
- Turbo mode: 5000 NN trials + 50 FEA validations in ~12 minutes
- Separate nn_study.db to avoid overloading dashboard

Performance Results (bracket_pareto_3obj study):
- NN prediction errors: mass 1-5%, stress 1-4%, stiffness 5-15%
- Found minimum mass designs at boundary (angle~30deg, thick~30mm)
- 100x speedup vs pure FEA exploration

Protocol Operating System:
- Add .claude/skills/ with Bootstrap, Cheatsheet, Context Loader
- Add docs/protocols/ with operations (OP_01-06) and system (SYS_10-14)
- Update SYS_14_NEURAL_ACCELERATION.md with MLP Turbo Mode docs

NX Automation:
- Add optimization_engine/hooks/ for NX CAD/CAE automation
- Add study_wizard.py for guided study creation
- Fix FEM mesh update: load idealized part before UpdateFemodel()

New Study:
- bracket_pareto_3obj: 3-objective Pareto (mass, stress, stiffness)
- 167 FEA trials + 5000 NN trials completed
- Demonstrates full hybrid workflow

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-06 20:01:59 -05:00