Normalizing Flows for Bayesian Experimental Design in Imaging Applications

TitleNormalizing Flows for Bayesian Experimental Design in Imaging Applications
Publication TypeConference
Year of Publication2024
AuthorsRafael Orozco, Abhinav Prakash Gahlot, Peng Chen, Mathias Louboutin, Felix J. Herrmann
Conference NameSIAM Conference on Uncertainty Quantification
Keywordsexpected information gain, Normalizing flows, optimal experimental design, SIAM

Neural density estimators such as normalizing flows have shown promise for estimation of the Bayesian posterior in a variety of imaging problems. Few works have explored how to practically exploit the probabilistic information contained in the full Bayesian solution of the inverse problem. Here we explore a simple modification to conditional normalizing flow training that enables Bayesian experimental design without modifying existing architectures. Based on a relationship between the expected information gain and maximum-likelihood training of normalizing flows, we show that experimental design can be achieved with the same training objective. We first verify that our method maximizes the expected information gain using a stylized problem. Then, we demonstrate our method can solve imaging problems in large scale medical and seismic applications.


(SIAM UQ, Trieste)

Citation Keyorozco2024SIAMboed