Liquid Splash Modeling with Neural Networks

نویسندگان

  • Kiwon Um
  • Xiangyu Hu
  • Nils Thürey
چکیده

This paper proposes a new data-driven approach for modeling detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for uid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations. We use neural networks to model the regression of splash formation using a classi er together with a velocity modi cation term. More speci cally, we employ a heteroscedastic model for the velocity updates. Our simulation results demonstrate that our model signi cantly improves visual delity with a large amount of realistic droplet formation and yields splash detail much more e ciently than ner discretizations. We show this for two di erent spatial scales and simulation setups.

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عنوان ژورنال:
  • CoRR

دوره abs/1704.04456  شماره 

صفحات  -

تاریخ انتشار 2017