  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.
