Liquid Splash Modeling with Neural Networks
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Date
2018
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
@article{10.1111:cgf.13522,
journal = {Computer Graphics Forum},
title = {{Liquid Splash Modeling with Neural Networks}},
author = {Um, Kiwon and Hu, Xiangyu and Thuerey, Nils},
year = {2018},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13522}
}