Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3424-3433, 2017.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Accelerating Eulerian Fluid Simulation With Convolutional Networks %A Jonathan Tompson %A Kristofer Schlachter %A Pablo Sprechmann %A Ken Perlin %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-tompson17a %I PMLR %P 3424--3433 %U https://proceedings.mlr.press/v70/tompson17a.html %V 70 %X 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.
APA
Tompson, J., Schlachter, K., Sprechmann, P. & Perlin, K.. (2017). Accelerating Eulerian Fluid Simulation With Convolutional Networks. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:3424-3433 Available from https://proceedings.mlr.press/v70/tompson17a.html.

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