Solving inverse problems using data-driven models

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Solving inverse problems using data-driven models

Arridge, S; Maass, P; Öktem, O; Schönlieb, CB; (2019) Solving inverse problems using data-driven models. Acta Numerica , 28 pp. 1-174. 10.1017/S0962492919000059. Green open access

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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.

Type:	Article
Title:	Solving inverse problems using data-driven models
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1017/S0962492919000059
Publisher version:	https://doi.org/10.1017/S0962492919000059
Language:	English
Additional information:	© The Author(s) 2019. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/).
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10083845