Liquid Splash Modeling with Neural Networks

dc.contributor.authorUm, Kiwonen_US
dc.contributor.authorHu, Xiangyuen_US
dc.contributor.authorThuerey, Nilsen_US
dc.contributor.editorThuerey, Nils and Beeler, Thaboen_US
dc.date.accessioned2018-07-23T10:07:45Z
dc.date.available2018-07-23T10:07:45Z
dc.date.issued2018
dc.description.abstractThis 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.en_US
dc.description.number8
dc.description.sectionheadersFluids with Particles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13522
dc.identifier.issn1467-8659
dc.identifier.pages171-182
dc.identifier.urihttps://doi.org/10.1111/cgf.13522
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13522
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectSupervised learning by regression
dc.titleLiquid Splash Modeling with Neural Networksen_US
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