Space-time continuous pde forecasting using equivariant neural fields

Abstract

Building on recent work using neural fields as representation for PDE solving, we investigate how to incorporate symmetries that often occur in physical data into a framework for continuous PDE solving by using Equivariant Neural Fields. We obtain impressive performance increases, especially over complicated geometries, which we attribute to the marked reduction in modelling complexity when respecting a PDE’s symmetries.