Accelerating eulerian fluid simulation with convolutional networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Research output: Contribution to conferencePaper

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.

Original languageEnglish (US)
StatePublished - Jan 1 2019
Event5th International Conference on Learning Representations, ICLR 2017 - Toulon, France
Duration: Apr 24 2017Apr 26 2017

Conference

Conference5th International Conference on Learning Representations, ICLR 2017
CountryFrance
CityToulon
Period4/24/174/26/17

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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  • Cite this

    Tompson, J., Schlachter, K., Sprechmann, P., & Perlin, K. (2019). Accelerating eulerian fluid simulation with convolutional networks. Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France.