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Solving inverse problems using data-driven models
UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England..
Univ Bremen, Dept Math, Postfach 330 440, D-28344 Bremen, Germany..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0002-1118-6483
Univ Cambridge, Dept Appl Math & Theoret Phys, Wilberforce Rd, Cambridge CB3 0WA, England..
2019 (English)In: Acta Numerica, ISSN 0962-4929, E-ISSN 1474-0508, Vol. 28, p. 1-174Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Cambridge University Press, 2019. Vol. 28, p. 1-174
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-263681DOI: 10.1017/S0962492919000059ISI: 000491992100001Scopus ID: 2-s2.0-85072936738OAI: oai:DiVA.org:kth-263681DiVA, id: diva2:1368706
Note

QC 20191108

Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2022-06-26Bibliographically approved

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Öktem, Ozan

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