1 citations · 1 across the 6 of their papers we have counts for
6 papers
Factorized Neural Implicit DMD for Parametric Dynamics
Siyuan Chen, Zhecheng Wang, Yixin Chen +4
A data-driven, model-free approach to modeling the temporal evolution of physical systems mitigates the need for explicit knowledge of the governing equations. Even when physical p…
Low-Rank Koopman Deformables with Log-Linear Time Integration
Yue Chang, Peter Yichen Chen, Eitan Grinspun +1
We present a low-rank Koopman operator formulation for accelerating deformable subspace simulation. Using a Dynamic Mode Decomposition (DMD) parameterization of the Koopman operato…
Precise Gradient Discontinuities in Neural Fields for Subspace Physics
Mengfei Liu, Yue Chang, Zhecheng Wang +2
Discontinuities in spatial derivatives appear in a wide range of physical systems, from creased thin sheets to materials with sharp stiffness transitions. Accurately modeling these…
Fast Subspace Fluid Simulation with a Temporally-Aware Basis
Siyuan Chen, Yixin Chen, Jonathan Panuelos +4
We present a novel reduced-order fluid simulation technique leveraging Dynamic Mode Decomposition (DMD) to achieve fast, memory-efficient, and user-controllable subspace simulation…
Shape Space Spectra
Yue Chang, Otman Benchekroun, Maurizio M. Chiaramonte +2
Eigenanalysis of differential operators, such as the Laplace operator or elastic energy Hessian, is typically restricted to a single shape and its discretization, limiting reduced…
Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction
Hrishikesh Viswanath, Yue Chang, Aleksey Panas +3
Simulating physical systems governed by Lagrangian dynamics often entails solving partial differential equations (PDEs) over high-resolution spatial domains, leading to significant…