Soft robot's natural dynamics calls for the development of tailored modeling techniques for
control. However, the high-dimensional configuration space of the geometrically exact …

We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for
rigged garments through deep learning. Classical approaches rely on Physically Based …

We propose the first reduced model simulation framework for deformable solid dynamics
using autoencoder neural networks. We provide a data‐driven approach to generating …

This paper provides a new avenue for exploiting deep neural networks to improve physics-
based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep …

Data-driven methods for physical simulation are an attractive option for interactive
applications due to their ability to trade precomputation and memory footprint in exchange …

We present SoftSMPL, a learning‐based method to model realistic soft‐tissue dynamics as a
function of body shape and motion. Datasets to learn such task are scarce and expensive to …

We propose a novel method to machine-learn highly detailed, nonlinear contact
deformations for real-time dynamic simulation. We depart from previous deformation …

Data driven models of human poses and soft-tissue deformations can produce very realistic
results, but they only model the visible surface of the human body and cannot create skin …

For a given PDE problem, three main factors affect the accuracy of FEM solutions: basis
order, mesh resolution, and mesh element quality. The first two factors are easy to control …

This paper introduces a novel subspace method for the simulation of dynamic deformations.
The method augments existing linear handle-based subspace formulations with nonlinear …