Bayesian inversion for tomography through machine learning

Ozan Íktem


The talk will outline recent approaches for using (deep) convolutional neural networks to solve a wide range of large-scale inverse problems, such as tomographic image reconstruction. Emphasis is on using neural networks for reconstruction that are domain adapted in the sense that they include physics based models for how data is generated. The talk will also discuss recent developments in using generative adversarial networks for uncertainty quantification in inverse problems.