Modeling of heterogeneous materials and media is a problem of fundamental importance to
a wide class of phenomena and systems, ranging from condensed matter physics, soft …

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 …

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 …

Robotic cutting of soft materials is critical for applications such as food processing,
household automation, and surgical manipulation. As in other areas of robotics, simulators …

Abstract We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset
consists of over one million fractured objects simulated from ten thousand base models. The …

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 …

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 RoboNinja, a learning-based cutting system for multi-material objects (ie, soft
objects with rigid cores such as avocados or mangos). In contrast to prior works using open …

Automated shape repair approaches currently lack access to datasets that describe real-
world damaged geometry. We present Fantastic Breaks (and Where to Find Them …

The proliferation of 3D representations, from explicit meshes to implicit neural fields and
more, motivates the need for simulators agnostic to representation. We present a data …