A new variational flow model with iterative prior updating and adaptive FNO surrogate for dimension-reduced Bayesian inference in high-dimensional PDE-governed inverse problems, reporting competitive accuracy versus MCMC, UKI, and SVGD on test cases.
The unknown fieldm ξ(x) is mod- eled as a Gaussian random field parameterized via a truncated Karhunen- Lo` eve (KL) expansion
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Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
A new variational flow model with iterative prior updating and adaptive FNO surrogate for dimension-reduced Bayesian inference in high-dimensional PDE-governed inverse problems, reporting competitive accuracy versus MCMC, UKI, and SVGD on test cases.