A likelihood-free normalizing-flow model with a learned summary network estimates 706-D log-conductivity posteriors from sparse noisy time-series head data at a fraction of online inference cost.
An introduction to inverse problems with applications
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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
A likelihood-free normalizing-flow model with a learned summary network estimates 706-D log-conductivity posteriors from sparse noisy time-series head data at a fraction of online inference cost.