| --- |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - meshgraphnet |
| - graph-neural-network |
| - cfd |
| - cylinder-flow |
| - custom_code |
| --- |
| |
| # MeshGraphNet — cylinder_flow |
| |
| [MeshGraphNet](https://arxiv.org/abs/2010.03409) (DeepMind, ICLR 2021) for flow around a cylinder. |
| Given the velocity at every mesh node, it predicts the velocity change for the next time step. |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModel |
| |
| model = AutoModel.from_pretrained("ketiswp/meshgraphnet-hf", trust_remote_code=True) |
|
|
| inputs = model.prepare_inputs(cells, mesh_pos, node_type, velocity) # one cylinder_flow time step |
| delta = model(**inputs).delta_velocity # [N, 2] |
| trajectory = model.rollout(**inputs, num_steps=599) # [600, N, 2] |
| ``` |
| |
| ## Training |
| |
| Trained on DeepMind `cylinder_flow` with the original settings: one frame per step (frames 1..598, |
| shuffle buffer 10000), velocity noise std 0.02, Adam with lr `1e-4 * 0.1^(step/5e6) + 1e-6`, 10M steps, |
| first 1000 steps only collect normalizer statistics, Sonnet weight init, float32. |
| |
| `split_first_linear` holds the first Linear of each layer per input |
| (`W [h_s; h_r; e] = W_s h_s + W_r h_r + W_e e`), so sender/receiver projections are computed once per |
| node instead of building the per-edge concatenation. Same weights and outputs as without it. |
| |
| ## Results |
| |
| CylinderFlow, in the format of Table 1 of the paper. |
| |
| | | # nodes (avg.) | # steps | t_model ms/step | t_full ms/step | t_GT ms/step | RMSE 1-step ×10⁻³ | RMSE rollout-50 ×10⁻³ | RMSE rollout-all ×10⁻³ | GPU | |
| |---|---|---|---|---|---|---|---|---|---| |
| | paper | 1885 | 600 | 21 | 23 | 820 | 2.34 ± 0.12 | 6.3 ± 0.7 | 40.88 ± 7.2 | NVIDIA Tesla V100 | |
| | this model | 1885 | 600 | 4.0 | 4.4 | – | 2.35 ± 0.09 | 6.67 ± 0.30 | 55.83 ± 3.97 | NVIDIA RTX 6000 Ada Generation | |
| |
| This model: 100 test trajectories; ± is the standard error over trajectories. Timings on one GPU, |
| batch 1, float32; `t_full` includes rebuilding the graph from the mesh every step. |
| |
| ## License |
| |
| Apache-2.0, as the original [DeepMind](https://github.com/google-deepmind/deepmind-research/tree/master/meshgraphnets) |
| and [medav/meshgraphnets-torch](https://github.com/medav/meshgraphnets-torch) code. |
| |
