Preconditioned training of normalizing flows for variational inference in inverse problems

TitlePreconditioned training of normalizing flows for variational inference in inverse problems
Publication TypeConference
Year of Publication2021
AuthorsAli Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A. Witte, Felix J. Herrmann
Conference Name3rd Symposium on Advances in Approximate Bayesian Inference
KeywordsInverse problems, Normalizing flows, Uncertainty quantification

Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth. To meet these challenges, we propose a preconditioning scheme involving a conditional normalizing flow (NF) capable of sampling from a low-fidelity posterior distribution directly. This conditional NF is used to speed up the training of the high-fidelity objective involving minimization of the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density for indirect measurements at hand. To minimize costs associated with the forward operator, we initialize the high-fidelity NF with the weights of the pretrained low-fidelity NF, which is trained beforehand on available model and data pairs. Our numerical experiments, including a 2D toy and a seismic compressed sensing example, demonstrate that thanks to the preconditioning considerable speed-ups are achievable compared to training NFs from scratch.



Citation Keysiahkoohi2021AABIpto