Liquid Splash Modeling with Neural Networks

Kiwon Um, Xiangyu Hu, Nils Thuerey

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for the fluid-implicit-particle method using training data acquired from physically parametrized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations.

Original languageEnglish
Pages (from-to)171-182
Number of pages12
JournalComputer Graphics Forum
Volume37
Issue number8
DOIs
StatePublished - Dec 2018

Keywords

  • CCS Concepts
  • Supervised learning by regression
  • •Computing methodologies → Physical simulation

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