We propose a method for the data‐driven inference of temporal evolutions of physical
functions with deep learning. More specifically, we target fluid flow problems, and we …

Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

Recently the Affine Particle-In-Cell (APIC) Method was proposed by Jiang et al.[2015;
2017b] to improve the accuracy of the transfers in Particle-In-Cell (PIC)[Harlow 1964] …

A variety of structures in nature exhibit sparse, thin, and intricate features. It is challenging to
investigate these structural characteristics using conventional numerical approaches since …

We propose a technique to simulate granular materials that exploits the dual strengths of
discrete and continuum treatments. Discrete element simulations provide unmatched levels …

The material point method (MPM) recently demonstrated its efficacy at simulating many
materials and the coupling between them on a massive scale. However, in scenarios …

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 …

We propose a novel scheme for simulating two-way coupled interactions between nonlinear
elastic solids and incompressible fluids. The key ingredient of this approach is a ghost matrix …

We present an adaptive Generalized Interpolation Material Point (GIMP) method for
simulating elastoplastic materials. Our approach allows adaptive refining and coarsening of …

This paper introduces a new weighting scheme for particle-grid transfers that generates
hybrid Lagrangian/Eulerian fluid simulations with uniform particle distributions and precise …