We propose a hybrid neural network and physics framework for reduced-order modeling of
elastoplasticity and fracture. State-of-the-art scientific computing models like the Material …

Learning-based approaches to cloth simulation have started to show their potential in recent
years. However, handling collisions and intersections in neural simulations remains a …

This paper develops a shape space framework for collision-aware geometric modeling,
where basic geometric operations automatically avoid inter-penetration. Shape spaces are a …

Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the
behavior of a system using a simplified kinematic representation. Typically, ROM is trained …

Reduced-order simulation is an emerging method for accelerating physical simulations with
high DOFs, and recently developed neural-network-based methods with nonlinear …

Traditional character animation specializes in characters with a rigidly articulated skeleton
and a bipedal/quadripedal morphology. This assumption simplifies the design of physically …

High-fidelity simulation of fluid dynamics is challenging because of the high dimensional
state data needed to capture fine details and the large computational cost associated with …

This paper presents a learning-based method for the simulation of rich contact deformations
on reduced deformation models. Previous works learn deformation models for specific pairs …

Realistic hair motion is crucial for high-quality avatars, but it is often limited by the
computational resources available for real-time applications. To address this challenge, we …

Formulating the dynamics of continuously deformable objects and other mechanical systems
analytically from first principles is an exceedingly challenging task, often impractical in real …