# AtomizerField Training Data

**Study Name**: uav_arm_atomizerfield_test
**Generated**: 2025-11-25 12:01:15

## Directory Structure

```
uav_arm_test/
├── trial_0001/
│   ├── input/
│   │   └── model.bdf      # NX Nastran input deck (BDF format)
│   ├── output/
│   │   └── model.op2      # NX Nastran binary results (OP2 format)
│   └── metadata.json      # Design parameters, objectives, constraints
├── trial_0002/
│   └── ...
├── study_summary.json     # Overall study metadata
└── README.md              # This file
```

## Design Variables

- beam_half_core_thickness
- beam_face_thickness
- holes_diameter
- hole_count

## Objectives

- mass
- fundamental_frequency

## Constraints

- max_displacement_limit
- max_stress_limit
- min_frequency_limit

## Usage with AtomizerField

### 1. Parse Training Data

```bash
cd Atomizer-Field
python batch_parser.py --data-dir "C:\Users\antoi\Documents\Atomaste\Atomizer\atomizer_field_training_data\uav_arm_test"
```

This converts BDF/OP2 files to PyTorch Geometric format.

### 2. Validate Parsed Data

```bash
python validate_parsed_data.py
```

### 3. Train Neural Network

```bash
python train.py --data-dir "training_data/parsed/" --epochs 200
```

### 4. Use Trained Model in Atomizer

```bash
cd ../Atomizer
python run_optimization.py --config studies/uav_arm_atomizerfield_test/workflow_config.json --use-neural
```

## File Formats

- **BDF (.bdf)**: Nastran Bulk Data File - contains mesh, materials, loads, BCs
- **OP2 (.op2)**: Nastran Output2 - binary results with displacements, stresses, etc.
- **metadata.json**: Human-readable trial metadata

## AtomizerField Documentation

See `Atomizer-Field/docs/` for complete documentation on:
- Neural network architecture
- Training procedures
- Integration with Atomizer
- Uncertainty quantification

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*Generated by Atomizer Training Data Exporter*