Liquid Splash Modeling with Neural Networks
This work addresses the challenge of enhancing visual realism in liquid simulations for applications like computer graphics and visual effects, though it is incremental as it builds on existing fluid simulation methods.
The paper tackles the problem of generating realistic small-scale splash details in liquid simulations by proposing a data-driven neural network approach that learns from high-resolution physical simulations, resulting in significantly improved visual fidelity and more efficient droplet formation compared to finer discretizations.
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.