# AtomizerField Neural Field Data Parser

**Version 1.0.0**

A production-ready Python parser that converts NX Nastran FEA results into standardized neural field training data for the AtomizerField optimization platform.

## What This Does

Instead of extracting just scalar values (like maximum stress) from FEA results, this parser captures **complete field data** - stress, displacement, and strain at every node and element. This enables neural networks to learn the physics of how structures respond to loads, enabling 1000x faster optimization with true physics understanding.

## Features

- ✅ **Complete Field Extraction**: Captures displacement, stress, strain at ALL points
- ✅ **Future-Proof Format**: Versioned data structure (v1.0) designed for years of neural network training
- ✅ **Efficient Storage**: Uses HDF5 for large arrays, JSON for metadata
- ✅ **Robust Parsing**: Handles mixed element types (solid, shell, beam, rigid)
- ✅ **Data Validation**: Built-in physics and quality checks
- ✅ **Batch Processing**: Process hundreds of cases automatically
- ✅ **Production Ready**: Error handling, logging, provenance tracking

## Quick Start

### 1. Installation

```bash
# Install dependencies
pip install -r requirements.txt
```

### 2. Prepare Your NX Nastran Analysis

In NX:
1. Create geometry and generate mesh
2. Apply materials (MAT1 cards)
3. Define boundary conditions (SPC)
4. Apply loads (FORCE, PLOAD4, GRAV)
5. Run **SOL 101** (Linear Static)
6. Request output: `DISPLACEMENT=ALL`, `STRESS=ALL`, `STRAIN=ALL`

### 3. Organize Files

```bash
mkdir training_case_001
mkdir training_case_001/input
mkdir training_case_001/output

# Copy files
cp your_model.bdf training_case_001/input/model.bdf
cp your_model.op2 training_case_001/output/model.op2
```

### 4. Run Parser

```bash
# Parse single case
python neural_field_parser.py training_case_001

# Validate results
python validate_parsed_data.py training_case_001
```

### 5. Check Output

You'll get:
- **neural_field_data.json** - Complete metadata and structure
- **neural_field_data.h5** - Large arrays (mesh, field results)

## Usage Guide

### Single Case Parsing

```bash
python neural_field_parser.py <case_directory>
```

**Expected directory structure:**
```
training_case_001/
├── input/
│   ├── model.bdf          # Nastran input deck
│   └── model.sim          # (optional) NX simulation file
├── output/
│   ├── model.op2          # Binary results (REQUIRED)
│   └── model.f06          # (optional) ASCII results
└── metadata.json          # (optional) Your design annotations
```

**Output:**
```
training_case_001/
├── neural_field_data.json  # Metadata, structure, small arrays
└── neural_field_data.h5    # Large arrays (coordinates, fields)
```

### Batch Processing

Process multiple cases at once:

```bash
python batch_parser.py ./training_data
```

**Expected structure:**
```
training_data/
├── case_001/
│   ├── input/model.bdf
│   └── output/model.op2
├── case_002/
│   ├── input/model.bdf
│   └── output/model.op2
└── case_003/
    ├── input/model.bdf
    └── output/model.op2
```

**Options:**
```bash
# Skip validation (faster)
python batch_parser.py ./training_data --no-validate

# Stop on first error
python batch_parser.py ./training_data --stop-on-error
```

**Output:**
- Parses all cases
- Validates each one
- Generates `batch_processing_summary.json` with results

### Data Validation

```bash
python validate_parsed_data.py training_case_001
```

Checks:
- ✓ File existence and format
- ✓ Data completeness (all required fields)
- ✓ Physics consistency (equilibrium, units)
- ✓ Data quality (no NaN/inf, reasonable values)
- ✓ Mesh integrity
- ✓ Material property validity

## Data Structure v1.0

The parser produces a standardized data structure designed to be future-proof:

```json
{
  "metadata": {
    "version": "1.0.0",
    "created_at": "timestamp",
    "analysis_type": "SOL_101",
    "units": {...}
  },
  "mesh": {
    "statistics": {
      "n_nodes": 15432,
      "n_elements": 8765
    },
    "nodes": {
      "ids": [...],
      "coordinates": "stored in HDF5"
    },
    "elements": {
      "solid": [...],
      "shell": [...],
      "beam": [...]
    }
  },
  "materials": [...],
  "boundary_conditions": {
    "spc": [...],
    "mpc": [...]
  },
  "loads": {
    "point_forces": [...],
    "pressure": [...],
    "gravity": [...],
    "thermal": [...]
  },
  "results": {
    "displacement": "stored in HDF5",
    "stress": "stored in HDF5",
    "strain": "stored in HDF5",
    "reactions": "stored in HDF5"
  }
}
```

### HDF5 Structure

Large numerical arrays are stored in HDF5 for efficiency:

```
neural_field_data.h5
├── mesh/
│   ├── node_coordinates    [n_nodes, 3]
│   └── node_ids            [n_nodes]
└── results/
    ├── displacement        [n_nodes, 6]
    ├── displacement_node_ids
    ├── stress/
    │   ├── ctetra_stress/
    │   │   ├── data        [n_elem, n_components]
    │   │   └── element_ids
    │   └── cquad4_stress/...
    ├── strain/...
    └── reactions/...
```

## Adding Design Metadata

Create a `metadata.json` in each case directory to track design parameters:

```json
{
  "design_parameters": {
    "thickness": 2.5,
    "fillet_radius": 5.0,
    "rib_height": 15.0
  },
  "optimization_context": {
    "objectives": ["minimize_weight", "minimize_stress"],
    "constraints": ["max_displacement < 2mm"],
    "iteration": 42
  },
  "notes": "Baseline design with standard loading"
}
```

See [metadata_template.json](metadata_template.json) for a complete template.

## Preparing NX Nastran Analyses

### Required Output Requests

Add these to your Nastran input deck or NX solution setup:

```nastran
DISPLACEMENT = ALL
STRESS = ALL
STRAIN = ALL
SPCFORCES = ALL
```

### Recommended Settings

- **Element Types**: CTETRA10, CHEXA20, CQUAD4
- **Analysis**: SOL 101 (Linear Static) initially
- **Units**: Consistent (recommend SI: mm, N, MPa, kg)
- **Output Format**: OP2 (binary) for efficiency

### Common Issues

**"OP2 has no results"**
- Ensure analysis completed successfully (check .log file)
- Verify output requests (DISPLACEMENT=ALL, STRESS=ALL)
- Check that OP2 file is not empty (should be > 1 KB)

**"Can't find BDF nodes"**
- Use .bdf or .dat file, not .sim
- Ensure mesh was exported to solver deck
- Check that BDF contains GRID cards

**"Memory error with large models"**
- Parser uses HDF5 chunking and compression
- For models > 100k elements, ensure you have sufficient RAM
- Consider splitting into subcases

## Loading Parsed Data

### In Python

```python
import json
import h5py
import numpy as np

# Load metadata
with open("neural_field_data.json", 'r') as f:
    metadata = json.load(f)

# Load field data
with h5py.File("neural_field_data.h5", 'r') as f:
    # Get node coordinates
    coords = f['mesh/node_coordinates'][:]

    # Get displacement field
    displacement = f['results/displacement'][:]

    # Get stress field
    stress = f['results/stress/ctetra_stress/data'][:]
    stress_elem_ids = f['results/stress/ctetra_stress/element_ids'][:]
```

### In PyTorch (for neural network training)

```python
import torch
from torch.utils.data import Dataset

class NeuralFieldDataset(Dataset):
    def __init__(self, case_directories):
        self.cases = []
        for case_dir in case_directories:
            h5_file = f"{case_dir}/neural_field_data.h5"
            with h5py.File(h5_file, 'r') as f:
                # Load inputs (mesh, BCs, loads)
                coords = torch.from_numpy(f['mesh/node_coordinates'][:])

                # Load outputs (displacement, stress fields)
                displacement = torch.from_numpy(f['results/displacement'][:])

                self.cases.append({
                    'coords': coords,
                    'displacement': displacement
                })

    def __len__(self):
        return len(self.cases)

    def __getitem__(self, idx):
        return self.cases[idx]
```

## Architecture & Design

### Why This Format?

1. **Complete Fields, Not Scalars**: Neural networks need to learn how stress/displacement varies across the entire structure, not just maximum values.

2. **Separation of Concerns**: JSON for structure/metadata (human-readable), HDF5 for numerical data (efficient).

3. **Future-Proof**: Versioned format allows adding new fields without breaking existing data.

4. **Physics Preservation**: Maintains all physics relationships (mesh topology, BCs, loads → results).

### Integration with Atomizer

This parser is Phase 1 of AtomizerField. Future integration:
- Phase 2: Neural network architecture (Graph Neural Networks)
- Phase 3: Training pipeline with physics-informed loss functions
- Phase 4: Integration with main Atomizer dashboard
- Phase 5: Production deployment for real-time optimization

## Troubleshooting

### Parser Errors

| Error | Solution |
|-------|----------|
| `FileNotFoundError: No model.bdf found` | Ensure BDF/DAT file exists in `input/` directory |
| `FileNotFoundError: No model.op2 found` | Ensure OP2 file exists in `output/` directory |
| `pyNastran read error` | Check BDF syntax, try opening in text editor |
| `OP2 subcase not found` | Ensure analysis ran successfully, check .f06 file |

### Validation Warnings

| Warning | Meaning | Action |
|---------|---------|--------|
| `No SPCs defined` | Model may be unconstrained | Check boundary conditions |
| `No loads defined` | Model has no loading | Add forces, pressures, or gravity |
| `Zero displacement` | Model not deforming | Check loads and constraints |
| `Very large displacement` | Possible rigid body motion | Add constraints or check units |

### Data Quality Issues

**NaN or Inf values:**
- Usually indicates analysis convergence failure
- Check .f06 file for error messages
- Verify model is properly constrained

**Mismatch in node counts:**
- Some nodes may not have results (e.g., rigid elements)
- Check element connectivity
- Validate mesh quality in NX

## Example Workflow

Here's a complete example workflow from FEA to neural network training data:

### 1. Create Parametric Study in NX

```bash
# Generate 10 design variants with different thicknesses
# Run each analysis with SOL 101
# Export BDF and OP2 files for each
```

### 2. Organize Files

```bash
mkdir parametric_study
for i in {1..10}; do
    mkdir -p parametric_study/thickness_${i}/input
    mkdir -p parametric_study/thickness_${i}/output
    # Copy BDF and OP2 files
done
```

### 3. Batch Parse

```bash
python batch_parser.py parametric_study
```

### 4. Review Results

```bash
# Check summary
cat parametric_study/batch_processing_summary.json

# Validate a specific case
python validate_parsed_data.py parametric_study/thickness_5
```

### 5. Load into Neural Network

```python
from torch.utils.data import DataLoader

dataset = NeuralFieldDataset([
    f"parametric_study/thickness_{i}" for i in range(1, 11)
])

dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

# Ready for training!
```

## Performance

Typical parsing times (on standard laptop):
- Small model (1k elements): ~5 seconds
- Medium model (10k elements): ~15 seconds
- Large model (100k elements): ~60 seconds
- Very large (1M elements): ~10 minutes

File sizes (compressed HDF5):
- Mesh (100k nodes): ~10 MB
- Displacement field (100k nodes × 6 DOF): ~5 MB
- Stress field (100k elements × 10 components): ~8 MB

## Requirements

- Python 3.8+
- pyNastran 1.4+
- NumPy 1.20+
- h5py 3.0+
- NX Nastran (any version that outputs .bdf and .op2)

## Files in This Repository

| File | Purpose |
|------|---------|
| `neural_field_parser.py` | Main parser - BDF/OP2 to neural field format |
| `validate_parsed_data.py` | Data validation and quality checks |
| `batch_parser.py` | Batch processing for multiple cases |
| `metadata_template.json` | Template for design parameter tracking |
| `requirements.txt` | Python dependencies |
| `README.md` | This file |
| `Context.md` | Project context and vision |
| `Instructions.md` | Original implementation instructions |

## Development

### Testing with Example Models

There are example models in the `Models/` folder. To test the parser:

```bash
# Set up test case
mkdir test_case_001
mkdir test_case_001/input
mkdir test_case_001/output

# Copy example files
cp Models/example_model.bdf test_case_001/input/model.bdf
cp Models/example_model.op2 test_case_001/output/model.op2

# Run parser
python neural_field_parser.py test_case_001

# Validate
python validate_parsed_data.py test_case_001
```

### Extending the Parser

To add new result types (e.g., modal analysis, thermal):

1. Update `extract_results()` in `neural_field_parser.py`
2. Add corresponding validation in `validate_parsed_data.py`
3. Update data structure version if needed
4. Document changes in this README

### Contributing

This is part of the AtomizerField project. When making changes:
- Preserve the v1.0 data format for backwards compatibility
- Add comprehensive error handling
- Update validation checks accordingly
- Test with multiple element types
- Document physics assumptions

## Future Enhancements

Planned features:
- [ ] Support for nonlinear analyses (SOL 106)
- [ ] Modal analysis results (SOL 103)
- [ ] Thermal analysis (SOL 153)
- [ ] Contact results
- [ ] Composite material support
- [ ] Automatic mesh quality assessment
- [ ] Parallel batch processing
- [ ] Progress bars for long operations
- [ ] Integration with Atomizer dashboard

## License

Part of the Atomizer optimization platform.

## Support

For issues or questions:
1. Check this README and troubleshooting section
2. Review `Context.md` for project background
3. Examine example files in `Models/` folder
4. Check pyNastran documentation for BDF/OP2 specifics

## Version History

### v1.0.0 (Current)
- Initial release
- Complete BDF/OP2 parsing
- Support for solid, shell, beam elements
- HDF5 + JSON output format
- Data validation
- Batch processing
- Physics consistency checks

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**AtomizerField**: Revolutionizing structural optimization through neural field learning.

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