Search Results for author: Peter Yichen Chen

Found 7 papers, 0 papers with code

Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics

no code implementations27 Apr 2023 Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik

Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models).

Out-of-Distribution Generalization

PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification

no code implementations9 Mar 2023 Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming Lin, Chenfanfu Jiang, Chuang Gan

In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology.

Neural Rendering Object

Implicit Neural Spatial Representations for Time-dependent PDEs

no code implementations30 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.

Contact mechanics

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations

no code implementations6 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.

Model reduction for the material point method via an implicit neural representation of the deformation map

no code implementations25 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.


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