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Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson

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. I will discuss our proposed method in some detail as well as it's limitations and future work.

Speaker: Jonathan Tompson, Google

Monday, 02/26/18


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Green Earth Sciences Building

367 Panama St, Room 104
Stanford University
Stanford, CA 94305

Website: Click to Visit