Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

We extend the incremental potential contact (IPC) model for contacting elastodynamics to
resolve systems composed of codimensional DOFs in arbitrary combination. This enables a …

We introduce Neural Marching Cubes, a data-driven approach for extracting a triangle mesh
from a discretized implicit field. We base our meshing approach on Marching Cubes (MC) …

Harnessing the power of modern multi-GPU architectures, we present a massively parallel
simulation system based on the Material Point Method (MPM) for simulating physical …

We introduce vertex block descent, a block coordinate descent solution for the variational
form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local …

In this paper, we wish to push the limit of real-time cloth and deformable body simulation to a
higher level with 50K to 500K vertices, based on the development of a novel GPU-based …

Newton's method has been a popular choice [Baraff and Witkin 1998] for solving the
variational form [Kane et al. 2000; Martin et al. 2011] associated with various deformable …

Computing constrained mappings between domains is a fundamental task, performed
across a wide range of geometric and physical applications ranging from parameterization …

This paper introduces BFEMP, a new approach for monolithically coupling the Material Point
Method (MPM) with the Finite Element Method (FEM) through barrier energy-based particle …

In this paper, we propose a neural network-based approach for learning to represent the
behavior of plastic solid materials ranging from rubber and metal to sand and snow. Unlike …