4 papers · 1 filter
Incremental Tensor-Train Compression from Streaming TT-Formatted Data with Applications to Reduced-Order Modeling of Linear Kinetic Equations
Wei Guo, Shuyuan Shang, Zhichao Peng
To mitigate the computational burden due to the high dimensionality of linear kinetic equations, low-rank solvers and data-driven reduced order models (ROMs) have been developed. L…
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…