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eabcc4c3ca
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refactor: Major reorganization of optimization_engine module structure
BREAKING CHANGE: Module paths have been reorganized for better maintainability.
Backwards compatibility aliases with deprecation warnings are provided.
New Structure:
- core/ - Optimization runners (runner, intelligent_optimizer, etc.)
- processors/ - Data processing
- surrogates/ - Neural network surrogates
- nx/ - NX/Nastran integration (solver, updater, session_manager)
- study/ - Study management (creator, wizard, state, reset)
- reporting/ - Reports and analysis (visualizer, report_generator)
- config/ - Configuration management (manager, builder)
- utils/ - Utilities (logger, auto_doc, etc.)
- future/ - Research/experimental code
Migration:
- ~200 import changes across 125 files
- All __init__.py files use lazy loading to avoid circular imports
- Backwards compatibility layer supports old import paths with warnings
- All existing functionality preserved
To migrate existing code:
OLD: from optimization_engine.nx_solver import NXSolver
NEW: from optimization_engine.nx.solver import NXSolver
OLD: from optimization_engine.runner import OptimizationRunner
NEW: from optimization_engine.core.runner import OptimizationRunner
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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2025-12-29 12:30:59 -05:00 |
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Antoine
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96b196de58
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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>
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2025-12-10 08:44:04 -05:00 |
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