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
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Stanford, CA 94305
Website: Click to Visit