Computer Graphics Forum (SCA 2018), Vol. 37 (8)

Authors
Kiwon Um, Technical University of Munich
Xiangyu Hu, Technical University of Munich
Nils Thuerey, Technical University of Munich

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 parameterized, 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.

Keywords
splash, neural networks, machine learning, liquid simulation, fluid-implicit-particle (FLIP)

Links
Wiley Online Library, Preprint
Video (YouTube)
Code (Coming Soon…)
Talk

This work is supported by the ERC Starting Grant 637014.