6 papers · 1 filter
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…
Interpolated Adaptive Linear Reduced Order Modeling for Deformation Dynamics
Yutian Tao, Maurizio Chiaramonte, Pablo Fernandez
Linear reduced-order modeling (ROM) is widely used for efficient simulation of deformation dynamics, but its accuracy is often limited by the fixed linearization of the reduced map…
Force-Dual Modes: Subspace Design from Stochastic Forces
Otman Benchekroun, Eitan Grinspun, Maurizio Chiaramonte +1
Designing subspaces for Reduced Order Modeling (ROM) is crucial for accelerating finite element simulations in graphics and engineering. Unfortunately, it's not always clear which…
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…
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
Zeshun Zong, Xuan Li, Minchen Li +6
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material…
LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields
Yue Chang, Peter Yichen Chen, Zhecheng Wang +3
Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained…