42 lines
1.9 KiB
Markdown
42 lines
1.9 KiB
Markdown
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# AtomizerField - Neural Field Predictor (Concept Archive)
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> **Status**: Concept archived. Code removed during repo cleanup (Feb 2026).
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> Original location: `atomizer-field/`
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## Idea
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Instead of extracting just scalar values (max stress, mass) from FEA results, capture **complete field data** - stress, displacement, and strain at every node and element. Train a GNN to predict full fields from design parameters, enabling 1000x faster optimization with true physics understanding.
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## Architecture (Two-Phase)
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```
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Phase 1: Data Pipeline
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NX Nastran (.bdf, .op2) → neural_field_parser.py → Neural Field Format (JSON + HDF5)
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Phase 2: Neural Network
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Graph representation → GNN training (train.py + field_predictor.py) → Field predictions (5-50ms)
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```
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## Key Components
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- **neural_field_parser.py** - Parsed BDF/OP2 into complete field data (displacement, stress, strain at ALL nodes/elements)
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- **train.py** - GNN training pipeline using PyTorch Geometric
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- **predict.py** - Inference for field predictions
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- **optimization_interface.py** - Integration with Atomizer optimization loop
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## Data Format
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- HDF5 for large arrays (field values per node)
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- JSON for metadata (mesh topology, material properties, BCs)
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- Versioned format (v1.0) designed for neural network training
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## Relationship to Current GNN Module
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The `optimization_engine/gnn/` module (ZernikeGNN) is the evolved version of this concept, specialized for mirror Zernike coefficient prediction rather than full-field prediction. If full-field prediction is needed in the future, this concept provides the architecture blueprint.
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## Training Data Requirements
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- SOL 101 (Linear Static) results with DISPLACEMENT=ALL, STRESS=ALL, STRAIN=ALL
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- Organized as training cases with input (.bdf) and output (.op2) pairs
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- The `atomizer_field_training_data/` directory contained ~66MB of sample training data (also removed)
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