Accelerating eulerian fluid simulation with convolutional networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Efficient simulation of the Navicr-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.

Original languageEnglish (US)
Title of host publication34th International Conference on Machine Learning, ICML 2017
PublisherInternational Machine Learning Society (IMLS)
Pages5258-5267
Number of pages10
ISBN (Electronic)9781510855144
StatePublished - 2017
Event34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia
Duration: Aug 6 2017Aug 11 2017

Publication series

Name34th International Conference on Machine Learning, ICML 2017
Volume7

Other

Other34th International Conference on Machine Learning, ICML 2017
CountryAustralia
CitySydney
Period8/6/178/11/17

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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  • Cite this

    Tompson, J., Schlachter, K., Sprechmann, P., & Perlin, K. (2017). Accelerating eulerian fluid simulation with convolutional networks. In 34th International Conference on Machine Learning, ICML 2017 (pp. 5258-5267). (34th International Conference on Machine Learning, ICML 2017; Vol. 7). International Machine Learning Society (IMLS).