Um, KiwonHu, XiangyuThuerey, NilsThuerey, Nils and Beeler, Thabo2018-07-232018-07-2320181467-8659https://doi.org/10.1111/cgf.13522https://diglib.eg.org:443/handle/10.1111/cgf13522This 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.Computing methodologiesPhysical simulationSupervised learning by regression10.1111/cgf.13522171-182