3 papers
physics.comp-ph2026
Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems
Shaoqian Zhou, Ling Guo, Xuhui Meng
Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving…
physics.comp-ph2026
Self-supervised neural operator for solving partial differential equations
Wen You, Shaoqian Zhou, Xuhui Meng
Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical…
physics.comp-ph2026
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
Shaoqian Zhou, Wen You, Ling Guo +1
Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. H…