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.

本文提出了一种新的基于数据驱动的方法，使用神经网络模拟液体模拟中的小尺度跳跃细节。使用物理参数化高分辨率模拟的训练数据，利用神经网络模拟分类器和速度调节器来回归跳跃形成。通过使用异方差模型对速度进行修改，我们证明了此方法在提高视觉保真度方面具有显著的效果，比更细的离散化更高效地产生跳跃细节。