Deep Learning for Simulation

Zhitao Ying · Tailin Wu · Peter Battaglia · Rose Yu · Ryan P Adams · Jure Leskovec

Abstract Workshop Website
Fri 7 May, 8:45 a.m. PDT


Recently there has been a surge in interest in using deep learning to facilitate simulation, in application areas including physics, chemistry, robotics and graphics.
We define simulation as the process of iteratively generating output of the next time step using the output of the previous time step as input starting from an initial condition. The primary motivation of the workshop is thus to encourage knowledge sharing and communication. Recent works have started to actively explore the potential of using deep learning to improve these highly important simulations in terms of accuracy and efficiency. We believe that this workshop will bring these communities together, create communication and collaboration, in order to speed-up research on this important topic.

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