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"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"]
}
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