feat: Merge Atomizer-Field neural network module into main repository

Permanently integrates the Atomizer-Field GNN surrogate system:
- neural_models/: Graph Neural Network for FEA field prediction
- batch_parser.py: Parse training data from FEA exports
- train.py: Neural network training pipeline
- predict.py: Inference engine for fast predictions

This enables 600x-2200x speedup over traditional FEA by replacing
expensive simulations with millisecond neural network predictions.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

atomizer-field/atomizer_field_config.yaml

@@ -0,0 +1,239 @@
+# AtomizerField Configuration
+# Long-term vision configuration for neural field learning
+
+# ============================================================================
+# Model Architecture
+# ============================================================================
+model:
+  type: "graph_neural_network"
+  architecture: "message_passing"
+
+  # Foundation model settings (for transfer learning)
+  foundation:
+    enabled: false  # Set to true when foundation model available
+    path: "models/physics_foundation_v1.pt"
+    freeze: true    # Freeze foundation layers during fine-tuning
+
+  # Adaptation layers (for fine-tuning on new component types)
+  adaptation:
+    layers: 2
+    neurons: 128
+    dropout: 0.1
+
+  # Core GNN parameters
+  gnn:
+    node_feature_dim: 12  # [x,y,z, BC(6), loads(3)]
+    edge_feature_dim: 5   # [E, nu, rho, G, alpha]
+    hidden_dim: 128
+    num_layers: 6
+    dropout: 0.1
+
+  # Output decoders
+  decoders:
+    displacement:
+      enabled: true
+      output_dim: 6  # [ux, uy, uz, rx, ry, rz]
+
+    stress:
+      enabled: true
+      output_dim: 6  # [sxx, syy, szz, txy, tyz, txz]
+
+# ============================================================================
+# Training Configuration
+# ============================================================================
+training:
+  # Progressive training (coarse to fine meshes)
+  progressive:
+    enabled: false  # Enable for multi-resolution training
+    stages:
+      - resolution: "coarse"
+        max_nodes: 5000
+        epochs: 20
+        lr: 0.001
+
+      - resolution: "medium"
+        max_nodes: 20000
+        epochs: 10
+        lr: 0.0005
+
+      - resolution: "fine"
+        max_nodes: 100000
+        epochs: 5
+        lr: 0.0001
+
+  # Online learning (during optimization)
+  online:
+    enabled: false  # Enable to learn from FEA during optimization
+    update_frequency: 10  # Update model every N FEA runs
+    quick_update_steps: 10
+    learning_rate: 0.0001
+
+  # Physics-informed loss weights
+  loss:
+    type: "physics"  # Options: mse, relative, physics, max
+    weights:
+      data: 1.0           # Match FEA results
+      equilibrium: 0.1    # ∇·σ + f = 0
+      constitutive: 0.1   # σ = C:ε
+      boundary: 1.0       # u = 0 at fixed nodes
+
+  # Standard training parameters
+  hyperparameters:
+    epochs: 100
+    batch_size: 4
+    learning_rate: 0.001
+    weight_decay: 0.00001
+
+  # Optimization
+  optimizer:
+    type: "AdamW"
+    betas: [0.9, 0.999]
+
+  scheduler:
+    type: "ReduceLROnPlateau"
+    factor: 0.5
+    patience: 10
+
+  # Early stopping
+  early_stopping:
+    enabled: true
+    patience: 50
+    min_delta: 0.0001
+
+# ============================================================================
+# Data Pipeline
+# ============================================================================
+data:
+  # Data normalization
+  normalization:
+    enabled: true
+    method: "standard"  # Options: standard, minmax
+
+  # Data augmentation
+  augmentation:
+    enabled: false  # Enable for data augmentation
+    techniques:
+      - rotation  # Rotate mesh randomly
+      - scaling   # Scale loads
+      - noise     # Add small noise to inputs
+
+  # Multi-resolution support
+  multi_resolution:
+    enabled: false
+    resolutions: ["coarse", "medium", "fine"]
+
+  # Caching
+  cache:
+    in_memory: false  # Cache dataset in RAM (faster but memory-intensive)
+    disk_cache: true  # Cache preprocessed graphs to disk
+
+# ============================================================================
+# Optimization Interface
+# ============================================================================
+optimization:
+  # Gradient-based optimization
+  use_gradients: true
+
+  # Uncertainty quantification
+  uncertainty:
+    enabled: false  # Enable ensemble for uncertainty
+    ensemble_size: 5
+    threshold: 0.1  # Recommend FEA if uncertainty > threshold
+
+  # FEA fallback
+  fallback_to_fea:
+    enabled: true
+    conditions:
+      - high_uncertainty  # Uncertainty > threshold
+      - extrapolation     # Outside training distribution
+      - critical_design   # Final validation
+
+  # Batch evaluation
+  batch_size: 100  # Evaluate designs in batches for speed
+
+# ============================================================================
+# Model Versioning & Deployment
+# ============================================================================
+deployment:
+  # Model versioning
+  versioning:
+    enabled: true
+    format: "semantic"  # v1.0.0, v1.1.0, etc.
+
+  # Model registry
+  registry:
+    path: "models/"
+    naming: "{component_type}_v{version}.pt"
+
+  # Metadata tracking
+  metadata:
+    track_training_data: true
+    track_performance: true
+    track_hyperparameters: true
+
+  # Production settings
+  production:
+    device: "cuda"  # cuda or cpu
+    batch_inference: true
+    max_batch_size: 100
+
+# ============================================================================
+# Integration with Atomizer
+# ============================================================================
+atomizer_integration:
+  # Dashboard integration
+  dashboard:
+    enabled: false  # Future: Show field visualizations in dashboard
+
+  # Database integration
+  database:
+    enabled: false  # Future: Store predictions in Atomizer DB
+
+  # API endpoints
+  api:
+    enabled: false  # Future: REST API for predictions
+    port: 8000
+
+# ============================================================================
+# Monitoring & Logging
+# ============================================================================
+monitoring:
+  # TensorBoard
+  tensorboard:
+    enabled: true
+    log_dir: "runs/tensorboard"
+
+  # Weights & Biases (optional)
+  wandb:
+    enabled: false
+    project: "atomizerfield"
+    entity: "your_team"
+
+  # Logging level
+  logging:
+    level: "INFO"  # DEBUG, INFO, WARNING, ERROR
+    file: "logs/atomizerfield.log"
+
+# ============================================================================
+# Experimental Features
+# ============================================================================
+experimental:
+  # Nonlinear analysis
+  nonlinear:
+    enabled: false
+
+  # Contact analysis
+  contact:
+    enabled: false
+
+  # Composite materials
+  composites:
+    enabled: false
+
+  # Modal analysis
+  modal:
+    enabled: false
+
+  # Topology optimization
+  topology:
+    enabled: false