Flow-ABI

Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems

Announce Type: new Abstract: Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving a new inference problem for each observation set, causing substantial computational costs that hinder real-time applications like online monitoring and digital twins. Furthermore, inferring over infinite-dimensional function spaces with varying observation sets poses major challenges...