Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation layers that lift physical field data into a latent representation suitable for dynamic processing. A typical choice for this layer is a convolutional encoder, which lifts field information in a locality-aware way and can be trained very efficiently. However, this choice cannot be applied to a large class of scientific computing problems where data are represented on unstructured meshes, for example simulations on domains with curved boundaries. Such data typically require graph neural network architectures, which are more expensive to train ...
| Authors | Georg Maierhofer, Miles Cranmer, Payel Mukhopadhyay |
|---|---|
| Primary category | cs.CE |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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