Atomizer/docs/04_USER_GUIDES/ATOMIZER_FIELD_NEURAL_OPTIMIZATION_GUIDE.md

AtomizerField Neural Optimization Guide

🚀 Overview

This guide explains how to use AtomizerField neural network surrogates with Atomizer to achieve 600x-500,000x speedup in FEA-based optimization. By replacing expensive 30-minute FEA simulations with 50ms neural network predictions, you can explore 1000x more design configurations in the same time.

Table of Contents

1. Quick Start
2. Architecture
3. Configuration
4. Training Data Collection
5. Neural Model Training
6. Hybrid Optimization Strategies
7. Performance Monitoring
8. Troubleshooting
9. Best Practices

Quick Start

1. Enable Neural Surrogate in Your Study

Add the following to your workflow_config.json:

{
  "neural_surrogate": {
    "enabled": true,
    "model_checkpoint": "atomizer-field/checkpoints/your_model.pt",
    "confidence_threshold": 0.85
  }
}

2. Use Neural-Enhanced Runner

# In your run_optimization.py
from optimization_engine.runner_with_neural import create_neural_runner

# Create neural-enhanced runner instead of standard runner
runner = create_neural_runner(
    config_path=Path("workflow_config.json"),
    model_updater=update_nx_model,
    simulation_runner=run_simulation,
    result_extractors=extractors
)

# Run optimization with automatic neural acceleration
study = runner.run(n_trials=1000)  # Can now afford 1000s of trials!

3. Monitor Speedup

After optimization, you'll see:

============================================================
NEURAL NETWORK SPEEDUP SUMMARY
============================================================
Trials using neural network: 950/1000 (95.0%)
Average NN inference time: 0.052 seconds
Average NN confidence: 92.3%
Estimated speedup: 34,615x
Time saved: ~475.0 hours
============================================================

Architecture

Component Overview

┌─────────────────────────────────────────────────────────┐
│                    Atomizer Framework                    │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  ┌──────────────────┐      ┌─────────────────────┐    │
│  │ Optimization     │      │  Neural-Enhanced    │    │
│  │ Runner           │ ───> │  Runner             │    │
│  └──────────────────┘      └─────────────────────┘    │
│           │                          │                  │
│           │                          ▼                  │
│           │                 ┌─────────────────────┐    │
│           │                 │  Neural Surrogate   │    │
│           │                 │  Manager            │    │
│           │                 └─────────────────────┘    │
│           │                          │                  │
│           ▼                          ▼                  │
│  ┌──────────────────┐      ┌─────────────────────┐    │
│  │  NX FEA Solver   │      │  AtomizerField NN   │    │
│  │  (30 minutes)    │      │  (50 ms)            │    │
│  └──────────────────┘      └─────────────────────┘    │
│                                                          │
└─────────────────────────────────────────────────────────┘

Key Components

1. NeuralOptimizationRunner (optimization_engine/runner_with_neural.py)

   • Extends base runner with neural capabilities
   • Manages hybrid FEA/NN decisions
   • Tracks performance metrics

2. NeuralSurrogate (optimization_engine/neural_surrogate.py)

   • Loads AtomizerField models
   • Converts design variables to graph format
   • Provides confidence-based predictions

3. HybridOptimizer (optimization_engine/neural_surrogate.py)

   • Implements smart switching strategies
   • Manages exploration/exploitation phases
   • Handles model retraining

4. TrainingDataExporter (optimization_engine/training_data_exporter.py)

   • Exports FEA results for neural training
   • Saves .dat/.op2 files with metadata

Configuration

Complete Neural Configuration

{
  "study_name": "advanced_optimization_with_neural",

  "neural_surrogate": {
    "enabled": true,
    "model_checkpoint": "atomizer-field/checkpoints/model_v2.0/best.pt",
    "confidence_threshold": 0.85,
    "fallback_to_fea": true,

    "ensemble_models": [
      "atomizer-field/checkpoints/model_v2.0/fold_1.pt",
      "atomizer-field/checkpoints/model_v2.0/fold_2.pt",
      "atomizer-field/checkpoints/model_v2.0/fold_3.pt"
    ],

    "device": "cuda",
    "batch_size": 32,
    "cache_predictions": true,
    "cache_size": 10000
  },

  "hybrid_optimization": {
    "enabled": true,
    "exploration_trials": 30,
    "training_interval": 100,
    "validation_frequency": 20,
    "min_training_samples": 50,

    "phases": [
      {
        "name": "exploration",
        "trials": [0, 30],
        "use_nn": false,
        "description": "Initial FEA exploration"
      },
      {
        "name": "exploitation",
        "trials": [31, 950],
        "use_nn": true,
        "description": "Neural network exploitation"
      },
      {
        "name": "validation",
        "trials": [951, 1000],
        "use_nn": false,
        "description": "Final FEA validation"
      }
    ],

    "adaptive_switching": true,
    "drift_threshold": 0.15,
    "retrain_on_drift": true
  },

  "training_data_export": {
    "enabled": true,
    "export_dir": "atomizer_field_training_data/my_study",
    "include_failed_trials": false,
    "compression": "gzip"
  }
}

Configuration Parameters

neural_surrogate

• enabled: Enable/disable neural surrogate
• model_checkpoint: Path to trained PyTorch model
• confidence_threshold: Minimum confidence to use NN (0.0-1.0)
• fallback_to_fea: Use FEA when confidence is low
• ensemble_models: List of models for ensemble predictions
• device: "cuda" or "cpu"
• batch_size: Batch size for neural inference
• cache_predictions: Cache NN predictions for repeated designs

hybrid_optimization

• exploration_trials: Number of initial FEA trials
• training_interval: Retrain NN every N trials
• validation_frequency: Validate NN with FEA every N trials
• phases: List of optimization phases with different strategies
• adaptive_switching: Dynamically adjust FEA/NN usage
• drift_threshold: Max prediction error before retraining

Training Data Collection

Automatic Export During Optimization

Training data is automatically exported when enabled:

{
  "training_data_export": {
    "enabled": true,
    "export_dir": "atomizer_field_training_data/beam_study"
  }
}

Directory structure created:

atomizer_field_training_data/beam_study/
├── trial_0001/
│   ├── input/
│   │   └── model.bdf      # NX Nastran input
│   ├── output/
│   │   └── model.op2      # Binary results
│   └── metadata.json      # Design vars, objectives
├── trial_0002/
│   └── ...
├── study_summary.json
└── README.md

Manual Export of Existing Studies

from optimization_engine.training_data_exporter import TrainingDataExporter

exporter = TrainingDataExporter(
    export_dir=Path("training_data/my_study"),
    study_name="beam_optimization",
    design_variable_names=["width", "height", "thickness"],
    objective_names=["max_stress", "mass"]
)

# Export each trial
for trial in existing_trials:
    exporter.export_trial(
        trial_number=trial.number,
        design_variables=trial.params,
        results=trial.values,
        simulation_files={
            'dat_file': Path(f"sim_{trial.number}.dat"),
            'op2_file': Path(f"sim_{trial.number}.op2")
        }
    )

exporter.finalize()

Neural Model Training

1. Prepare Training Data

cd atomizer-field
python batch_parser.py --data-dir ../atomizer_field_training_data/beam_study

This converts BDF/OP2 files to PyTorch Geometric format.

2. Train Neural Network

python train.py \
  --data-dir training_data/parsed/ \
  --epochs 200 \
  --model GraphUNet \
  --hidden-channels 128 \
  --num-layers 4 \
  --physics-loss-weight 0.3

3. Validate Model

python validate.py --checkpoint checkpoints/model_v2.0/best.pt

Expected output:

Validation Results:
- Mean Absolute Error: 2.34 MPa (1.2%)
- R² Score: 0.987
- Inference Time: 52ms ± 8ms
- Physics Constraint Violations: 0.3%

4. Deploy Model

Copy trained model to Atomizer:

cp checkpoints/model_v2.0/best.pt ../studies/my_study/neural_model.pt

Hybrid Optimization Strategies

Strategy 1: Phased Optimization

# Phase 1: Exploration (FEA)
# Collect diverse training data
for trial in range(30):
    use_fea()  # Always use FEA

# Phase 2: Training
# Train neural network on collected data
train_neural_network()

# Phase 3: Exploitation (NN)
# Use NN for rapid optimization
for trial in range(31, 980):
    if confidence > 0.85:
        use_nn()  # Fast neural network
    else:
        use_fea()  # Fallback to FEA

# Phase 4: Validation (FEA)
# Validate best designs with FEA
for trial in range(981, 1000):
    use_fea()  # Final validation

Strategy 2: Adaptive Switching

class AdaptiveStrategy:
    def should_use_nn(self, trial_number):
        # Start with exploration
        if trial_number < 20:
            return False

        # Check prediction accuracy
        if self.recent_error > 0.15:
            self.retrain_model()
            return False

        # Periodic validation
        if trial_number % 50 == 0:
            return False  # Validate with FEA

        # High-stakes decisions
        if self.near_optimal_region():
            return False  # Use FEA for critical designs

        return True  # Use NN for everything else

Strategy 3: Uncertainty-Based

def decide_solver(design_vars, ensemble_models):
    # Get predictions from ensemble
    predictions = [model.predict(design_vars) for model in ensemble_models]

    # Calculate uncertainty
    mean_pred = np.mean(predictions)
    std_pred = np.std(predictions)
    confidence = 1.0 - (std_pred / mean_pred)

    if confidence > 0.9:
        return "neural", mean_pred
    elif confidence > 0.7:
        # Mixed strategy
        if random.random() < confidence:
            return "neural", mean_pred
        else:
            return "fea", None
    else:
        return "fea", None

Performance Monitoring

Real-Time Metrics

The neural runner tracks performance automatically:

# During optimization
Trial 42: Used neural network (confidence: 94.2%, time: 0.048s)
Trial 43: Neural confidence too low (72.1%), using FEA
Trial 44: Used neural network (confidence: 91.8%, time: 0.051s)

Post-Optimization Analysis

# Access performance metrics
metrics = runner.neural_speedup_tracker

# Calculate statistics
avg_speedup = np.mean([m['speedup'] for m in metrics])
total_time_saved = sum([m['time_saved'] for m in metrics])

# Export detailed report
runner.export_performance_report("neural_performance.json")

Visualization

import matplotlib.pyplot as plt

# Plot confidence over trials
plt.figure(figsize=(10, 6))
plt.plot(trials, confidences, 'b-', label='NN Confidence')
plt.axhline(y=0.85, color='r', linestyle='--', label='Threshold')
plt.xlabel('Trial Number')
plt.ylabel('Confidence')
plt.title('Neural Network Confidence During Optimization')
plt.legend()
plt.savefig('nn_confidence.png')

# Plot speedup
plt.figure(figsize=(10, 6))
plt.bar(phases, speedups, color=['red', 'yellow', 'green'])
plt.xlabel('Optimization Phase')
plt.ylabel('Speedup Factor')
plt.title('Speedup by Optimization Phase')
plt.savefig('speedup_by_phase.png')

Troubleshooting

Common Issues and Solutions

1. Low Neural Network Confidence

WARNING: Neural confidence below threshold (65.3% < 85%)

Solutions:

• Train with more diverse data
• Reduce confidence threshold
• Use ensemble models
• Check if design is out of training distribution

2. Model Loading Error

ERROR: Could not load model checkpoint: file not found

Solutions:

• Verify path in config
• Check file permissions
• Ensure model is compatible with current AtomizerField version

3. Slow Neural Inference

WARNING: Neural inference taking 2.3s (expected <100ms)

Solutions:

• Use GPU acceleration (device: "cuda")
• Reduce batch size
• Enable prediction caching
• Check model complexity

4. Prediction Drift

WARNING: Neural predictions drifting from FEA (error: 18.2%)

Solutions:

• Retrain model with recent data
• Increase validation frequency
• Adjust drift threshold
• Check for distribution shift

Debugging Tips

1. Enable Verbose Logging

import logging
logging.basicConfig(level=logging.DEBUG)

2. Test Neural Model Standalone

from optimization_engine.neural_surrogate import NeuralSurrogate

surrogate = NeuralSurrogate(model_path="model.pt")
test_design = {"width": 50, "height": 75, "thickness": 5}
pred, conf, used_nn = surrogate.predict(test_design)
print(f"Prediction: {pred}, Confidence: {conf}")

3. Compare NN vs FEA

# Force FEA for comparison
fea_result = runner.simulation_runner(design_vars)
nn_result = runner.neural_surrogate.predict(design_vars)
error = abs(fea_result - nn_result) / fea_result * 100
print(f"Relative error: {error:.1f}%")

Best Practices

1. Data Quality

• Diverse Training Data: Ensure training covers full design space
• Quality Control: Validate FEA results before training
• Incremental Training: Continuously improve model with new data

2. Model Selection

• Start Simple: Begin with smaller models, increase complexity as needed
• Ensemble Methods: Use 3-5 models for robust predictions
• Physics Constraints: Include physics loss for better generalization

3. Optimization Strategy

• Conservative Start: Use high confidence threshold initially
• Adaptive Approach: Adjust strategy based on performance
• Validation: Always validate final designs with FEA

4. Performance Optimization

• GPU Acceleration: Use CUDA for 10x faster inference
• Batch Processing: Process multiple designs simultaneously
• Caching: Cache predictions for repeated designs

5. Safety and Reliability

• Fallback Mechanism: Always have FEA fallback
• Confidence Monitoring: Track and log confidence levels
• Periodic Validation: Regularly check NN accuracy

Example: Complete Workflow

Step 1: Initial FEA Study

# Run initial optimization with training data export
python run_optimization.py --trials 50 --export-training-data

Step 2: Train Neural Model

cd atomizer-field
python batch_parser.py --data-dir ../training_data
python train.py --epochs 200

Step 3: Neural-Enhanced Optimization

# Update config to use neural model
config["neural_surrogate"]["enabled"] = True
config["neural_surrogate"]["model_checkpoint"] = "model.pt"

# Run with 1000s of trials
runner = create_neural_runner(config_path, ...)
study = runner.run(n_trials=5000)  # Now feasible!

Step 4: Validate Results

# Get top 10 designs
best_designs = study.best_trials[:10]

# Validate with FEA
for design in best_designs:
    fea_result = validate_with_fea(design.params)
    print(f"Design {design.number}: NN={design.value:.2f}, FEA={fea_result:.2f}")

Parametric Surrogate Model (NEW)

The ParametricSurrogate is a design-conditioned GNN that predicts ALL 4 optimization objectives directly from design parameters, providing a future-proof solution for neural-accelerated optimization.

Key Features

• Predicts all objectives: mass, frequency, max_displacement, max_stress
• Design-conditioned: Takes design variables as explicit input
• Ultra-fast inference: ~4.5ms per prediction (vs ~10s FEA = 2000x speedup)
• GPU accelerated: Uses CUDA for fast inference

Quick Start

from optimization_engine.neural_surrogate import create_parametric_surrogate_for_study

# Create surrogate with auto-detection
surrogate = create_parametric_surrogate_for_study()

# Predict all objectives
test_params = {
    "beam_half_core_thickness": 7.0,
    "beam_face_thickness": 2.5,
    "holes_diameter": 35.0,
    "hole_count": 10.0
}

results = surrogate.predict(test_params)
print(f"Mass: {results['mass']:.2f} g")
print(f"Frequency: {results['frequency']:.2f} Hz")
print(f"Max displacement: {results['max_displacement']:.6f} mm")
print(f"Max stress: {results['max_stress']:.2f} MPa")
print(f"Inference time: {results['inference_time_ms']:.2f} ms")

Architecture

Design Parameters (4)
        |
   ┌────▼────┐
   │ Design  │
   │ Encoder │ (MLP: 4 -> 64 -> 128)
   └────┬────┘
        │
   ┌────▼────┐
   │  GNN    │ (Design-conditioned message passing)
   │ Layers  │ (4 layers, 128 hidden channels)
   └────┬────┘
        │
   ┌────▼────┐
   │ Global  │ (Mean + Max pooling)
   │ Pool    │
   └────┬────┘
        │
   ┌────▼────┐
   │ Scalar  │ (MLP: 384 -> 128 -> 64 -> 4)
   │ Heads   │
   └────┬────┘
        │
        ▼
  4 Objectives: [mass, frequency, displacement, stress]

Training the Parametric Model

cd atomizer-field
python train_parametric.py \
  --train_dir ../atomizer_field_training_data/uav_arm_train \
  --val_dir ../atomizer_field_training_data/uav_arm_val \
  --epochs 200 \
  --output_dir runs/parametric_model

Model Location

Trained models are stored in:

atomizer-field/runs/parametric_uav_arm_v2/
├── checkpoint_best.pt      # Best validation loss model
├── config.json             # Model configuration
└── training_log.csv        # Training history

Integration with Optimization

The ParametricSurrogate can be used as a drop-in replacement for FEA during optimization:

from optimization_engine.neural_surrogate import create_parametric_surrogate_for_study

# In your objective function
surrogate = create_parametric_surrogate_for_study()

def fast_objective(design_params):
    """Use neural network instead of FEA"""
    results = surrogate.predict(design_params)
    return results['mass'], results['frequency']

Advanced Topics

Custom Neural Architectures

See AtomizerField documentation for implementing custom GNN architectures.

Multi-Fidelity Optimization

Combine low-fidelity (coarse mesh) and high-fidelity (fine mesh) simulations with neural surrogates.

Transfer Learning

Use pre-trained models from similar problems to accelerate training.

Active Learning

Intelligently select which designs to evaluate with FEA for maximum learning.

Summary

AtomizerField neural surrogates enable:

• ✅ 600x-500,000x speedup over traditional FEA
• ✅ Explore 1000x more designs in same time
• ✅ Maintain accuracy with confidence-based fallback
• ✅ Seamless integration with existing Atomizer workflows
• ✅ Continuous improvement through online learning

Start with the Quick Start section and gradually adopt more advanced features as needed.

For questions or issues, see the AtomizerField GitHub or Atomizer documentation.