[Code] [Paper (arxiv)] [Codebase Demo Video] [BibTeX]

Universal Physics Transformers (UPTs) are a novel learning paradigm that can model a wide range of spatio-temporal problems - both for Lagrangian and Eulerian discretization schemes.

The architecture of UPT consists of an encoder, an approximator and a decoder. The encoder is responsible to encode the physics domain into a latent representation, the approximator propagates the latent representation forward in time and the decoder transforms the latent representation back to the physics domain.

To enforce the responsibilities of each component, inverse encoding and decoding tasks are added.

UPTs can model transient flow simulations (Eulerian discretization scheme) as indicated by test loss and rollout performance (measured via correlation time):

UPTs can also model the flow-field of particle based simulations (Lagrangian discretization scheme):

Particles show the ground truth velocities of particles and the white arrows show the learned velocity field of a UPT model evaluated on the positions of a regular grid.