1 citations · 2 across the 7 of their papers we have counts for
9 papers
Learning Optimal Robust-Pathwise Control of Elliptic Equations with Random Coefficients
Qi Sun, Yanhan Cui, Ling Guo +1
Conventional control approaches to optimization problems under uncertainty entail a practical compromise: deterministic controls offer robustness at the expense of pathwise accurac…
Physics-guided correction for operator learning under model misspecification
Lei Ma, Nicolas Boullé, Yu-Sen Yang +2
Physics-informed operator learning provides an efficient framework for approximating solution operators of partial differential equations by combining observational data with gover…
Latent representation learning based model correction and uncertainty quantification for PDEs
Wenwen Zhou, Xiaodong Feng, Ling Guo +1
Model correction is essential for reliable PDE learning when the governing physics is misspecified due to simplified assumptions or limited observations. In the machine learning li…
FNWoS: Fractional Neural Walk-on-Spheres Methods for High-Dimensional PDEs Driven by -stable Lévy Process on Irregular Domains
Ling Guo, Mingxin Qin, Changtao Sheng +2
In this paper, we develop a highly parallel and derivative-free fractional neural walk-on-spheres method (FNWoS) for solving high-dimensional fractional Poisson equations on irregu…
Deep set based operator learning with uncertainty quantification
Lei Ma, Ling Guo, Hao Wu +1
Learning operators from data is central to scientific machine learning. While DeepONets are widely used for their ability to handle complex domains, they require fixed sensor numbe…
LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
Xiaodong Feng, Ling Guo, Xiaoliang Wan +3
We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The…
Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
Xintong Wang, Xiaofei Guan, Ling Guo +1
Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems is a fundamental yet challenging problem in many fields of science and engineering. Existing methods…
Optimal Low-dimensional Approximation of Transfer Operators via Flow Matching: Computation and Error Analysis
Zhicheng Zhang, Ling Guo, Hao Wu
Reaction coordinates (RCs) are low-dimensional representations of complex dynamical systems that capture their long-term dynamics. In this work, we focus on the criteria of lumpabi…
Energy based diffusion generator for efficient sampling of Boltzmann distributions
Yan Wang, Ling Guo, Hao Wu +1
Sampling from Boltzmann distributions, particularly those tied to high dimensional and complex energy functions, poses a significant challenge in many fields. In this work, we pres…