5 papers
Incremental Tensor-Train Compression from Streaming TT-Formatted Data: Applications to Reduced-Order Modeling
Wei Guo, Zhichao Peng
High-dimensional tensor data streams arise naturally in scientific and engineering applications, such as simulations of kinetic equations and quantum systems, where samples become…
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
Yongsheng Chen, Wei Guo, Qi Tang +1
We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies latent-space discovery and dynamics learnin…
Hypernetwork-Conditioned WENO5 Conservative-Form CNNs for One-Dimensional Conservation Laws
Yongsheng Chen, Wei Guo, Xinghui Zhong
We study a conservative data-driven discretization for one-dimensional hyperbolic conservation laws based on the classical fifth-order WENO finite-volume scheme and a hypernetwork…
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
Yongsheng Chen, Yong Chen, Wei Guo +1
Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational d…
Physics-informed machine learning for reconstruction of dynamical systems with invariant measure score matching
Yongsheng Chen, Suddhasattwa Das, Wei Guo +1
In this paper, we develop a novel mesh-free framework, termed physics-informed neural networks with invariant measure score matching (PINN-IMSM), for reconstructing dynamical syste…