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 sparse 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.
BibTeX
@InProceedings{pmlr-v70-tompson17a,
title = {Accelerating {E}ulerian Fluid Simulation With Convolutional Networks},
author = {Jonathan Tompson and Kristofer Schlachter and Pablo Sprechmann and Ken Perlin},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3424--3433},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/tompson17a/tompson17a.pdf},
url = {https://proceedings.mlr.press/v70/tompson17a.html},
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 sparse 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.}
}