no code implementations • 29 Jan 2021 • Jingyu Chen, Victoria Kala, Alan Marquez-Razon, Elias Gueidon, David A. B. Hyde, Joseph Teran
We present a novel Material Point Method (MPM) discretization of surface tension forces that arise from spatially varying surface energies.
Graphics Computational Engineering, Finance, and Science I.3.0; I.6.0
no code implementations • 25 Jan 2022 • Yongxu Jin, Yushan Han, Zhenglin Geng, Joseph Teran, Ronald Fedkiw
We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks.
no code implementations • 22 May 2022 • Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Kala, David Hyde, Joseph Teran
We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations.
no code implementations • 29 Sep 2023 • Kai Weixian Lan, Elias Gueidon, Ayano Kaneda, Julian Panetta, Joseph Teran
The core of our solver is a neural network trained to approximate the inverse of a discrete structured-grid Laplace operator for a domain of arbitrary shape and with mixed boundary conditions.
no code implementations • 25 Apr 2024 • Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran, Abishek Kumar, Kenji Tashiro, Ronald Fedkiw
Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation.
