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>
Permanently integrates the Atomizer-Field GNN surrogate system:
- neural_models/: Graph Neural Network for FEA field prediction
- batch_parser.py: Parse training data from FEA exports
- train.py: Neural network training pipeline
- predict.py: Inference engine for fast predictions
This enables 600x-2200x speedup over traditional FEA by replacing
expensive simulations with millisecond neural network predictions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
