17 citations · 50 across the 9 of their papers we have counts for
9 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…
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…
Learning Preconditioner for Conjugate Gradient PDE Solvers
Yichen Li, Peter Yichen Chen, Tao Du +1
Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradi…
Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics
Pingchuan Ma, Peter Yichen Chen, Bolei Deng +4
We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations. Many NN approaches learn an end-to-end model that implici…
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
Xuan Li, Yi-Ling Qiao, Peter Yichen Chen +4
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a v…
Implicit Neural Spatial Representations for Time-dependent PDEs
Honglin Chen, Rundi Wu, Eitan Grinspun +2
Implicit Neural Spatial Representation (INSR) has emerged as an effective representation of spatially-dependent vector fields. This work explores solving time-dependent PDEs with I…
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho +6
The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduc…
Model reduction for the material point method via an implicit neural representation of the deformation map
Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun +1
This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the by approximating the deform…