Grid-pretrained physics models move onto unstructured meshes: swap the outer convolution, and cylinder flow runs zero-shot below most trained baselines
A mesh-native tokenisation layer that matches classical convolutions lets grid-pretrained physics foundation models deploy on unstructured meshes without retraining, reaching usable accuracy zero-shot and beating mesh baselines after a few fine-tuning epochs.