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cs.LG2026
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
Yueyang Wang, Xili Wang, Kejun Tang +3
Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecifi…
cs.LG2024
DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos
Kai Du, Yongle Xie, Tao Zhou +1
Classical particle methods based on propagation of chaos (PoC) have been developed for solving mean-field stochastic differential equations and their associated nonlinear Fokker--P…
cs.LG2023★ 1 cited
Adaptive importance sampling for Deep Ritz
Xiaoliang Wan, Tao Zhou, Yuancheng Zhou
We introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed…