3 papers
cs.LG2026
Harness In-Context Operator Learning with Chain of Operators
Minghui Yang, Ling Guo, Liu Yang
Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Net…
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
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…