# AtomizerField - Neural Field Predictor (Concept Archive)

> **Status**: Concept archived. Code removed during repo cleanup (Feb 2026).
> Original location: `atomizer-field/`

## Idea

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.

## Architecture (Two-Phase)

```
Phase 1: Data Pipeline
  NX Nastran (.bdf, .op2) → neural_field_parser.py → Neural Field Format (JSON + HDF5)

Phase 2: Neural Network
  Graph representation → GNN training (train.py + field_predictor.py) → Field predictions (5-50ms)
```

## Key Components

- **neural_field_parser.py** - Parsed BDF/OP2 into complete field data (displacement, stress, strain at ALL nodes/elements)
- **train.py** - GNN training pipeline using PyTorch Geometric
- **predict.py** - Inference for field predictions
- **optimization_interface.py** - Integration with Atomizer optimization loop

## Data Format

- HDF5 for large arrays (field values per node)
- JSON for metadata (mesh topology, material properties, BCs)
- Versioned format (v1.0) designed for neural network training

## Relationship to Current GNN Module

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.

## Training Data Requirements

- SOL 101 (Linear Static) results with DISPLACEMENT=ALL, STRESS=ALL, STRAIN=ALL
- Organized as training cases with input (.bdf) and output (.op2) pairs
- The `atomizer_field_training_data/` directory contained ~66MB of sample training data (also removed)