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96b196de58725435ea5fe3ba566f09fa1e69768e
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6 Commits

Author SHA1 Message Date
Antoine
96b196de58 feat: Add Zernike GNN surrogate module and M1 mirror V12/V13 studies 
This commit introduces the GNN-based surrogate for Zernike mirror optimization
and the M1 mirror study progression from V12 (GNN validation) to V13 (pure NSGA-II).

## GNN Surrogate Module (optimization_engine/gnn/)

New module for Graph Neural Network surrogate prediction of mirror deformations:

- `polar_graph.py`: PolarMirrorGraph - fixed 3000-node polar grid structure
- `zernike_gnn.py`: ZernikeGNN with design-conditioned message passing
- `differentiable_zernike.py`: GPU-accelerated Zernike fitting and objectives
- `train_zernike_gnn.py`: ZernikeGNNTrainer with multi-task loss
- `gnn_optimizer.py`: ZernikeGNNOptimizer for turbo mode (~900k trials/hour)
- `extract_displacement_field.py`: OP2 to HDF5 field extraction
- `backfill_field_data.py`: Extract fields from existing FEA trials

Key innovation: Design-conditioned convolutions that modulate message passing
based on structural design parameters, enabling accurate field prediction.

## M1 Mirror Studies

### V12: GNN Field Prediction + FEA Validation
- Zernike GNN trained on V10/V11 FEA data (238 samples)
- Turbo mode: 5000 GNN predictions → top candidates → FEA validation
- Calibration workflow for GNN-to-FEA error correction
- Scripts: run_gnn_turbo.py, validate_gnn_best.py, compute_full_calibration.py

### V13: Pure NSGA-II FEA (Ground Truth)
- Seeds 217 FEA trials from V11+V12
- Pure multi-objective NSGA-II without any surrogate
- Establishes ground-truth Pareto front for GNN accuracy evaluation
- Narrowed blank_backface_angle range to [4.0, 5.0]

## Documentation Updates

- SYS_14: Added Zernike GNN section with architecture diagrams
- CLAUDE.md: Added GNN module reference and quick start
- V13 README: Study documentation with seeding strategy

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-10 08:44:04 -05:00
Antoine
3e9488d9f0 feat: Add Adaptive Method Selector for intelligent optimization strategy 
The AMS analyzes optimization problems and recommends the best method:
- ProblemProfiler: Static analysis of config (dimensions, objectives, constraints)
- EarlyMetricsCollector: Dynamic analysis from FEA trials (smoothness, correlations)
- AdaptiveMethodSelector: Rule-based scoring for method recommendations
- RuntimeAdvisor: Mid-run monitoring for method pivots

Key features:
- Analyzes problem characteristics (n_variables, n_objectives, constraints)
- Computes response smoothness and variable sensitivity from trial data
- Recommends TURBO, HYBRID_LOOP, PURE_FEA, or GNN_FIELD
- Provides confidence scores and suggested parameters
- CLI: python -m optimization_engine.method_selector <config> [db]

Documentation:
- Add SYS_15_METHOD_SELECTOR.md protocol
- Update CLAUDE.md with new system protocol reference

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-07 05:51:49 -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
Antoine
fb2d06236a feat: Improve dashboard layout and Claude terminal context 
- Reorganize dashboard: control panel on top, charts stacked vertically
- Add Set Context button to Claude terminal for study awareness
- Add conda environment instructions to CLAUDE.md
- Fix STUDY_REPORT.md location in generate-report.md skill
- Claude terminal now sends study context with skills reminder

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-04 20:59:31 -05:00
Antoine
8cbdbcad78 feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements 
## Protocol 13: Adaptive Multi-Objective Optimization
- Iterative FEA + Neural Network surrogate workflow
- Initial FEA sampling, NN training, NN-accelerated search
- FEA validation of top NN predictions, retraining loop
- adaptive_state.json tracks iteration history and best values
- M1 mirror study (V11) with 103 FEA, 3000 NN trials

## Dashboard Visualization Enhancements
- Added Plotly.js interactive charts (parallel coords, Pareto, convergence)
- Lazy loading with React.lazy() for performance
- Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB)
- Chart library toggle (Recharts default, Plotly on-demand)
- ExpandableChart component for full-screen modal views
- ConsoleOutput component for real-time log viewing

## Documentation
- Protocol 13 detailed documentation
- Dashboard visualization guide
- Plotly components README
- Updated run-optimization skill with Mode 5 (adaptive)

## Bug Fixes
- Fixed TypeScript errors in dashboard components
- Fixed Card component to accept ReactNode title
- Removed unused imports across components

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-04 07:41:54 -05:00
Anto01
e3bdb08a22 feat: Major update with validators, skills, dashboard, and docs reorganization 
- Add validation framework (config, model, results, study validators)
- Add Claude Code skills (create-study, run-optimization, generate-report,
  troubleshoot, analyze-model)
- Add Atomizer Dashboard (React frontend + FastAPI backend)
- Reorganize docs into structured directories (00-09)
- Add neural surrogate modules and training infrastructure
- Add multi-objective optimization support

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-25 19:23:58 -05:00