AI RESEARCH
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
arXiv CS.LG
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ArXi:2605.29373v1 Announce Type: new Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecified prior information. To address these issues, we propose a deep adaptive dimension-reduction Bayesian inference framework based on the Variational Flow (VF) model.