Variational Bayesian model updating using normalizing flows

==Abstract==

We investigate the use of normalizing flows to approximate transport maps from tractable reference densities to complex Bayesian posterior distributions for Bayesian model updating. A Gaussian process (GP) surrogate with active sampling is used to provide a differentiable target density for optimizing the transport map. While results show normalizing flows can capture multimodal behavior in a simple example, further work is needed to refine the active sampling strategy and enable mode identification in the GP surrogate for robust multimodal density approximation.

== Full Paper ==
<pdf>Media:Mett_et_al_2025a_5518_I250452_23_QK9T.pdf</pdf>