Solving inverse problems using data-driven models. (1st May 2019)

• Record Type: Journal Article
• Authors: Arridge, Simon; Maass, Peter; Öktem, Ozan; Schönlieb, Carola-Bibiane
• Is Part Of: Acta numerica. Volume 28(2019)
• Page Start: 1
• Page End: 174
• Publication Date: 2019-05-01
• Subjects: Numerical analysis -- Periodicals; 518
• DOI: 10.1017/S0962492919000059
• Languages: English
• ISSNs: 0962-4929

Abstract

Abstract : Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.