3 papers
cs.LG2026
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…
math.NA2026
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…
math.NA2024
Conservative semi-lagrangian finite difference scheme for transport simulations using graph neural networks
Yongsheng Chen, Wei Guo, Xinghui Zhong
Semi-Lagrangian (SL) schemes are highly efficient for simulating transport equations and are widely used across various applications. Despite their success, designing genuinely mul…