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

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