20cd66dff6
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>
26 lines
752 B
Python
26 lines
752 B
Python
"""
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AtomizerField Neural Models Package
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Phase 2: Neural Network Architecture for Field Prediction
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This package contains neural network models for learning complete FEA field results
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from mesh geometry, boundary conditions, and loads.
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Models:
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- AtomizerFieldModel: Full field predictor (displacement + stress fields)
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- ParametricFieldPredictor: Design-conditioned scalar predictor (mass, freq, disp, stress)
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"""
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__version__ = "2.0.0"
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# Import main model classes for convenience
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from .field_predictor import AtomizerFieldModel, create_model
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from .parametric_predictor import ParametricFieldPredictor, create_parametric_model
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__all__ = [
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'AtomizerFieldModel',
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'create_model',
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'ParametricFieldPredictor',
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'create_parametric_model',
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]
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