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Author SHA1 Message Date
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
Antoine
20cd66dff6 feat: Add parametric predictor and training script for AtomizerField 
Rebuilds missing neural network components based on documentation:

- neural_models/parametric_predictor.py: Design-conditioned GNN that
  predicts all 4 optimization objectives (mass, frequency, displacement,
  stress) directly from design parameters. ~500K trainable parameters.

- train_parametric.py: Training script with multi-objective loss,
  checkpoint saving with normalization stats, and TensorBoard logging.

- Updated __init__.py to export ParametricFieldPredictor and
  create_parametric_model for use by optimization_engine/neural_surrogate.py

These files enable the neural acceleration workflow:
1. Collect FEA training data (189 trials already collected)
2. Train parametric model: python train_parametric.py --train_dir ...
3. Run neural-accelerated optimization with --enable-nn flag

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-26 16:33:50 -05:00