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"model_name": "ClimODE",
"model_type": "climode",
"architectures": [
"ClimODE",
"ClimateEncoderFreeUncertain"
],
"framework": "PyTorch",
"domain": "atmosphere",
"task": "global-weather-forecasting-with-neural-odes",
"implementation": {
"entry_point": "model/climode.py",
"scope": "OneScience adapter of the physics-informed neural transport ODE with optional attention and probabilistic output; it is not an AutoModel-compatible Transformers implementation"
},
"architecture": {
"family": "physics-informed neural ODE with advective transport and convolutional velocity/noise networks",
"input_format": "BCHW",
"input_grid_shape": [
32,
64
],
"input_channels": 5,
"output_channels": 5,
"time_step_hours": 6,
"solver": "euler",
"absolute_tolerance": 0.005,
"relative_tolerance": 0.005,
"use_attention": true,
"use_uncertainty": true,
"use_positional_encoder": false,
"resnet_repetitions": [
5,
3,
2
],
"resnet_hidden_channels": [
128,
64,
10
],
"history_frames_for_context": 3
},
"data": {
"dataset": "ERA5",
"source_grid_shape": [
721,
1440
],
"model_grid_shape": [
32,
64
],
"regrid_method": "bilinear with periodic longitude handling",
"variables": [
"z",
"t",
"t2m",
"u10",
"v10"
],
"variable_sources": {
"z": "geopotential_500",
"t": "temperature_850",
"t2m": "2m_temperature",
"u10": "10m_u_component_of_wind",
"v10": "10m_v_component_of_wind"
},
"input_steps": 1,
"output_steps": 1,
"normalization": "ClimODE min-max normalization; source statistics are loaded from the configured static directory",
"uncertainty_output": "mean and standard deviation fields when use_uncertainty is enabled"
},
"configuration_sources": [
"conf/config.yaml",
"model/climode.py",
"scripts/data_loader.py",
"scripts/train.py"
]
}
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