On the Value of Tokeniser Pretraining in Physics Foundation Models
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings. The emerging class of physics foundation models typically aims to learn two tasks jointly: (i) extracting compact representations of high-resolution spatiotemporal data, and (ii) capturing governing physical dynamics. However, learning both tasks from scratch simultaneously can impede the effectiveness of either proce...
| Authors | Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana et al. |
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| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 11 March 2026 |
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