# Neural Acceleration Module

**Last Updated**: December 5, 2025
**Version**: 1.0
**Type**: Optional Module

This module provides guidance for AtomizerField neural network surrogate acceleration, enabling 1000x faster optimization by replacing expensive FEA evaluations with instant neural predictions.

---

## When to Load

- User needs >50 optimization trials
- User mentions "neural", "surrogate", "NN", "machine learning"
- User wants faster optimization
- Exporting training data for neural networks

---

## Overview

**Key Innovation**: Train once on FEA data, then explore 50,000+ designs in the time it takes to run 50 FEA trials.

| Metric | Traditional FEA | Neural Network | Improvement |
|--------|-----------------|----------------|-------------|
| Time per evaluation | 10-30 minutes | 4.5 milliseconds | **2,000-500,000x** |
| Trials per hour | 2-6 | 800,000+ | **1000x** |
| Design exploration | ~50 designs | ~50,000 designs | **1000x** |

---

## Training Data Export (PR.9)

Enable training data export in your optimization config:

```json
{
  "training_data_export": {
    "enabled": true,
    "export_dir": "atomizer_field_training_data/my_study"
  }
}
```

### Using TrainingDataExporter

```python
from optimization_engine.training_data_exporter import TrainingDataExporter

training_exporter = TrainingDataExporter(
    export_dir=export_dir,
    study_name=study_name,
    design_variable_names=['param1', 'param2'],
    objective_names=['stiffness', 'mass'],
    constraint_names=['mass_limit'],
    metadata={'atomizer_version': '2.0', 'optimization_algorithm': 'NSGA-II'}
)

# In objective function:
training_exporter.export_trial(
    trial_number=trial.number,
    design_variables=design_vars,
    results={'objectives': {...}, 'constraints': {...}},
    simulation_files={'dat_file': dat_path, 'op2_file': op2_path}
)

# After optimization:
training_exporter.finalize()
```

### Training Data Structure

```
atomizer_field_training_data/{study_name}/
├── trial_0001/
│   ├── input/model.bdf      # Nastran input (mesh + params)
│   ├── output/model.op2     # Binary results
│   └── metadata.json        # Design params + objectives
├── trial_0002/
│   └── ...
└── study_summary.json       # Study-level metadata
```

**Recommended**: 100-500 FEA samples for good generalization.

---

## Neural Configuration

### Full Configuration Example

```json
{
  "study_name": "bracket_neural_optimization",

  "surrogate_settings": {
    "enabled": true,
    "model_type": "parametric_gnn",
    "model_path": "models/bracket_surrogate.pt",
    "confidence_threshold": 0.85,
    "validation_frequency": 10,
    "fallback_to_fea": true
  },

  "training_data_export": {
    "enabled": true,
    "export_dir": "atomizer_field_training_data/bracket_study",
    "export_bdf": true,
    "export_op2": true,
    "export_fields": ["displacement", "stress"]
  },

  "neural_optimization": {
    "initial_fea_trials": 50,
    "neural_trials": 5000,
    "retraining_interval": 500,
    "uncertainty_threshold": 0.15
  }
}
```

### Configuration Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `enabled` | bool | false | Enable neural surrogate |
| `model_type` | string | "parametric_gnn" | Model architecture |
| `model_path` | string | - | Path to trained model |
| `confidence_threshold` | float | 0.85 | Min confidence for predictions |
| `validation_frequency` | int | 10 | FEA validation every N trials |
| `fallback_to_fea` | bool | true | Use FEA when uncertain |

---

## Model Types

### Parametric Predictor GNN (Recommended)

Direct optimization objective prediction - fastest option.

```
Design Parameters (ND) → Design Encoder (MLP) → GNN Backbone → Scalar Heads

Output (objectives):
├── mass (grams)
├── frequency (Hz)
├── max_displacement (mm)
└── max_stress (MPa)
```

**Use When**: You only need scalar objectives, not full field predictions.

### Field Predictor GNN

Full displacement/stress field prediction.

```
Input Features (12D per node):
├── Node coordinates (x, y, z)
├── Material properties (E, nu, rho)
├── Boundary conditions (fixed/free per DOF)
└── Load information (force magnitude, direction)

Output (per node):
├── Displacement (6 DOF: Tx, Ty, Tz, Rx, Ry, Rz)
└── Von Mises stress (1 value)
```

**Use When**: You need field visualization or complex derived quantities.

### Ensemble Models

Multiple models for uncertainty quantification.

```python
# Run N models
predictions = [model_i(x) for model_i in ensemble]

# Statistics
mean_prediction = np.mean(predictions)
uncertainty = np.std(predictions)

# Decision
if uncertainty > threshold:
    result = run_fea(x)  # Fall back to FEA
else:
    result = mean_prediction
```

---

## Hybrid FEA/Neural Workflow

### Phase 1: FEA Exploration (50-100 trials)
- Run standard FEA optimization
- Export training data automatically
- Build landscape understanding

### Phase 2: Neural Training
- Parse collected data
- Train parametric predictor
- Validate accuracy

### Phase 3: Neural Acceleration (1000s of trials)
- Use neural network for rapid exploration
- Periodic FEA validation
- Retrain if distribution shifts

### Phase 4: FEA Refinement (10-20 trials)
- Validate top candidates with FEA
- Ensure results are physically accurate
- Generate final Pareto front

---

## Training Pipeline

### Step 1: Collect Training Data

Run optimization with export enabled:
```bash
python run_optimization.py --train --trials 100
```

### Step 2: Parse to Neural Format

```bash
cd atomizer-field
python batch_parser.py ../atomizer_field_training_data/my_study
```

### Step 3: Train Model

**Parametric Predictor** (recommended):
```bash
python train_parametric.py \
  --train_dir ../training_data/parsed \
  --val_dir ../validation_data/parsed \
  --epochs 200 \
  --hidden_channels 128 \
  --num_layers 4
```

**Field Predictor**:
```bash
python train.py \
  --train_dir ../training_data/parsed \
  --epochs 200 \
  --model FieldPredictorGNN \
  --hidden_channels 128 \
  --num_layers 6 \
  --physics_loss_weight 0.3
```

### Step 4: Validate

```bash
python validate.py --checkpoint runs/my_model/checkpoint_best.pt
```

Expected output:
```
Validation Results:
├── Mean Absolute Error: 2.3% (mass), 1.8% (frequency)
├── R² Score: 0.987
├── Inference Time: 4.5ms ± 0.8ms
└── Physics Violations: 0.2%
```

### Step 5: Deploy

Update config to use trained model:
```json
{
  "neural_surrogate": {
    "enabled": true,
    "model_checkpoint": "atomizer-field/runs/my_model/checkpoint_best.pt",
    "confidence_threshold": 0.85
  }
}
```

---

## Uncertainty Thresholds

| Uncertainty | Action |
|-------------|--------|
| < 5% | Use neural prediction |
| 5-15% | Use neural, flag for validation |
| > 15% | Fall back to FEA |

---

## Accuracy Expectations

| Problem Type | Expected R² | Samples Needed |
|--------------|-------------|----------------|
| Well-behaved | > 0.95 | 50-100 |
| Moderate nonlinear | > 0.90 | 100-200 |
| Highly nonlinear | > 0.85 | 200-500 |

---

## AtomizerField Components

```
atomizer-field/
├── neural_field_parser.py       # BDF/OP2 parsing
├── field_predictor.py           # Field GNN
├── parametric_predictor.py      # Parametric GNN
├── train.py                     # Field training
├── train_parametric.py          # Parametric training
├── validate.py                  # Model validation
├── physics_losses.py            # Physics-informed loss
└── batch_parser.py              # Batch data conversion

optimization_engine/
├── neural_surrogate.py          # Atomizer integration
└── runner_with_neural.py        # Neural runner
```

---

## Troubleshooting

| Symptom | Cause | Solution |
|---------|-------|----------|
| High prediction error | Insufficient training data | Collect more FEA samples |
| Out-of-distribution warnings | Design outside training range | Retrain with expanded range |
| Slow inference | Large mesh | Use parametric predictor instead |
| Physics violations | Low physics loss weight | Increase `physics_loss_weight` |

---

## Cross-References

- **System Protocol**: [SYS_14_NEURAL_ACCELERATION](../../docs/protocols/system/SYS_14_NEURAL_ACCELERATION.md)
- **Operations**: [OP_05_EXPORT_TRAINING_DATA](../../docs/protocols/operations/OP_05_EXPORT_TRAINING_DATA.md)
- **Core Skill**: [study-creation-core](../core/study-creation-core.md)