Data-driven Models in Inverse Problems

Advances in learning-based methods are revolutionizing several fields in applied mathematics, including inverse problems, resulting in a major paradigm shift towards data-driven approaches. This volume, which is inspired by this cutting-edge area of research, brings together contributors from the inverse problem community and shows how to successfully combine model- and data-driven approaches to gain insight into practical and theoretical issues. - Combines classical approaches in regularization theory with deep neural networks. - Describes the Mathematical aspects of data-driven inverse problems. - Discusses specific applications of data-driven methods in inverse problems.