Deep learning for inverse problems. Abstracts from the workshop held March 7--13, 2021 (hybrid meeting) (Q2131206)

Summary: Machine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems.