Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Abstract: Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources. In this work, we propose a data-driven approach that leverages the approximation power of deep-learning with the precision of standard solvers to obtain fast and highly realistic simulations. Our method solves the incompressible Euler equations using the standard operator splitting method, in which a large linear system with many free-parameters must be solved. We use a Convolutional Network with a highly tailored architecture, trained using a novel unsupervised learning framework to solve the linear system. We present real-time 2D and 3D simulations that outperform recently proposed data-driven methods; the obtained results are realistic and show good generalization properties.

TL;DR: We present novel deep-learning architecture and training framework to solve the Navier-Stokes PDE for fluid flow.

Keywords: Deep learning, Applications

Conflicts: nyu.edu, google.com