# 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