Trajectory prediction has been widely pursued in many fields, and many model-based and
model-free methods have been explored. The former include rule-based, geometric or …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

We present a technique for automatically producing a deformation of an input triangle mesh,
guided solely by a text prompt. Our framework is capable of deformations that produce both …

We present a general framework for the garment animation problem through unsupervised
deep learning inspired in physically based simulation. Existing trends in the literature …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

This paper introduces a framework designed to accurately predict piecewise linear
mappings of arbitrary meshes via a neural network, enabling training and evaluating over …

We present a GPU algorithm for deformable simulation. Our method offers good
computational efficiency and penetration-free guarantee at the same time, which are not …

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 …

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 …

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 …