3 papers
cs.LG2026
Chain of Operators: An Inference-Time Harness for In-Context Operator Learning
Minghui Yang, Ling Guo, Chenghan Wu +1
While scientific foundation models show immense promise in accelerating physical simulations and numerical forecasting, they remain notoriously brittle when encountering out-of-dis…
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…