no code implementations • 26 Oct 2023 • Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture.
no code implementations • 30 Sep 2022 • Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen
Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.
no code implementations • 6 Jun 2022 • Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G A Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun
We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.
no code implementations • 25 Sep 2021 • Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg
Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold.
no code implementations • 15 Sep 2021 • Henrique Teles Maia, Chang Xiao, DIngzeyu Li, Eitan Grinspun, Changxi Zheng
We find that each layer component's evaluation produces an identifiable magnetic signal signature, from which layer topology, width, function type, and sequence order can be inferred using a suitably trained classifier and a joint consistency optimization based on integer programming.
1 code implementation • 14 Oct 2019 • Yun, Fei, Christopher Batty, Eitan Grinspun
In this report we discuss and propose a correction to a convergence and stability issue occurring in the work of Da et al.[2015], in which they proposed a numerical model to simulate soap bubbles.
Graphics I.3.7
no code implementations • 15 Jul 2016 • Yinxiao Li, Yan Wang, Yonghao Yue, Danfei Xu, Michael Case, Shih-Fu Chang, Eitan Grinspun, Peter Allen
A fully featured 3D model of the garment is constructed in real-time and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose.