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Article overview
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Liquid Splash Modeling with Neural Networks | | Kiwon Um
; Xiangyu Hu
; Nils Thuerey
; | | Date: |
14 Apr 2017 | | Abstract: | 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 fluid-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 classifier
together with a velocity modification term. More specifically, we employ a
heteroscedastic model for the velocity updates. 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. We show this for two different spatial
scales and simulation setups. | | Source: | arXiv, 1704.4456 | | Services: | Forum | Review | PDF | Favorites | |
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