| # GraphCast — grid ↔ icosahedral-mesh GNN (PyTorch) | |
| [← Model zoo](../README.md) · [All model pages](README.md) · **Paper:** *Science* 2023, [doi:10.1126/science.adi2336](https://doi.org/10.1126/science.adi2336) | |
| Independent re-implementation of the encode (Grid2Mesh) – process (multi-mesh message passing) – decode (Mesh2Grid) design. | |
| Stage-wise parity against DeepMind's JAX code and NVIDIA PhysicsNeMo at mesh levels 0–1 (graphs, Grid2Mesh, processor | |
| N ≤ 16, Mesh2Grid): [graphcast_parity.md](https://github.com/GISWLH/WeatherAI/blob/main/docs/graphcast_parity.md). Architecture notes: [graphcast_specs.md](https://github.com/GISWLH/WeatherAI/blob/main/docs/graphcast_specs.md). | |
| No official checkpoint is loaded and there is no whole-model parity claim. | |
| ```python | |
| import torch | |
| from weatherai.models import GraphCast_lite | |
| y = GraphCast_lite()(torch.randn(1, 4, 32, 64)) # tiny mesh (level 1), residual next step | |
| ``` | |
| Training smoke example: `examples/graphcast/train_smoke.py`. | |