From Molecular Dynamics to MeshGraphNets

Abstract

In this blog, we discuss the MeshGraphNets paper and its predecessor paper through the lens of the graph-learning paradigm. We claim that molecular dynamics and smoothed particle hydrodynamics are the ancestors of all graph-based, learned particle simulators and show how graph-based approaches naturally extend to meshes. Then, we compare MeshGraphNets to other approaches, both graph-based and not. Last but not least, we conclude by presenting the strengths and weaknesses of the model, directions for future work, and a code snippet of the core algorithm written in JAX.

Tasks

Reproductions