Data-Driven Viscosity Solver for Fluid Simulation

Abstract

We propose a data-driven viscosity solver based on U-shaped convolutional neural network to predict velocity changes due to viscosity. Our solver takes velocity derivatives, fluid volume, and solid indicator quantities as input. The traditional marker-and-cell (MAC) grid stores velocities at the edges of the grid, causing the dimensions of the velocity field vary from axis to axis. In our work, we suggest a symmetric MAC grid that maintains consistent dimensions across axes without interpolation or symmetry breaking. The proposed grid effectively transfers spatial fluid quantities such as partial derivatives of velocity, enabling networks to generate accurate predictions. Additionally, we introduce a physics-based loss inspired by the variational formulation of viscosity to enhance the network’s generalization for a wide range of viscosity coefficients. We demonstrate various fluid simulation results, including 2D and 3D fluid-rigid body scenes and a scene exhibiting the buckling effect.