{
  "model_name": "MassConservingCNN",
  "model_type": "massconservingcnn",
  "architectures": ["MassConservingCNN"],
  "framework": "PyTorch",
  "domain": "earth-science",
  "task": "mass-aware-data-assimilation-analysis",
  "implementation": {
    "entry_point": "model/massconservingcnn.py",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "input_shape": ["B", 4, 250],
    "output_shape": ["B", 3, 250],
    "variable_order": ["u", "h", "r"],
    "hidden_layers": 4,
    "filters_per_layer": 32,
    "kernel_size": 3,
    "padding": "circular",
    "influence_radius": 5
  },
  "paper_model": {
    "input_channels": 4,
    "output_channels": 3,
    "grid_points": 250,
    "hidden_channels": 32,
    "hidden_layers": 4,
    "kernel_size": 3,
    "hidden_activation": "SELU",
    "rain_activation": "ReLU",
    "padding": "circular",
    "influence_radius": 5,
    "train_samples": 48000,
    "validation_samples": 48000,
    "batch_size": 96,
    "epochs": 100,
    "optimizer": "Adam",
    "eta": 2.0,
    "experiment": "dT10_eta2"
  },
  "configuration_sources": ["conf/config.yaml", "model/massconservingcnn.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}