Universal Physics Transformers

arXiv:2402.12365v1 Announce Type: new
Abstract: Deep neural network based surrogates for partial differential equations have recently gained increased interest. However, akin to their numerical counterparts, different techniques are used across applications, even if the underlying dynamics of the systems are similar. A prominent example is the Lagrangian and Eulerian specification in computational fluid dynamics, posing a challenge for neural networks to effectively model particle- as opposed to grid-based dynamics. We introduce Universal Physics Transformers (UPTs), a novel learning paradigm which …

arxiv cs.ai cs.lg physics physics.flu-dyn transformers type