Solving inverse problems using data-driven models
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Arridge, S
Maass, P
Öktem, O
Schönlieb, CB
Abstract
jats:pRecent 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.</jats:p>
Description
Keywords
4901 Applied Mathematics, 49 Mathematical Sciences, 4905 Statistics, Generic health relevance
Journal Title
Acta Numerica
Conference Name
Journal ISSN
0962-4929
1474-0508
1474-0508
Volume Title
28
Publisher
Cambridge University Press (CUP)
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (EP/H023348/1)
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Leverhulme Trust (RPG-2018-121)
Leverhulme Trust (PLP-2017-275)
National Physical Laboratory (NPL) (Unknown)
Engineering and Physical Sciences Research Council (EP/J009539/1)
EPSRC (EP/S026045/1)
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Leverhulme Trust (RPG-2018-121)
Leverhulme Trust (PLP-2017-275)
National Physical Laboratory (NPL) (Unknown)
Engineering and Physical Sciences Research Council (EP/J009539/1)
EPSRC (EP/S026045/1)