It has been 25 years since Sulsky and her coworkers developed the first version of the
material point method (MPM): a quasi particle method to solve continuum mechanics …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

We introduce PhysGaussian a new method that seamlessly integrates physically grounded
Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis …

Realistic object interactions are crucial for creating immersive virtual experiences, yet
synthesizing realistic 3D object dynamics in response to novel interactions remains a …

Physical simulators have been widely used in robot planning and control. Among them,
differentiable simulators are particularly favored, as they can be incorporated into gradient …

In this paper, we introduce the Moving Least Squares Material Point Method (MLS-MPM).
MLS-MPM naturally leads to the formulation of Affine Particle-In-Cell (APIC)[Jiang et al …

We propose a hybrid neural network (NN) and PDE approach for learning generalizable
PDE dynamics from motion observations. Many NN approaches learn an end-to-end model …

Existing approaches to system identification (estimating the physical parameters of an
object) from videos assume known object geometries. This precludes their applicability in a …

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 introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …