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dc.contributor.authorBenning, Martin
dc.contributor.authorCelledoni, Elena
dc.contributor.authorEhrhardt, Matthias J.
dc.contributor.authorOwren, Brynjulf
dc.contributor.authorSchönlieb, Carola-Bibiane
dc.date.accessioned2020-01-23T12:24:52Z
dc.date.available2020-01-23T12:24:52Z
dc.date.created2019-05-15T11:21:13Z
dc.date.issued2019
dc.identifier.issn2158-2505
dc.identifier.urihttp://hdl.handle.net/11250/2637641
dc.description.abstractWe consider recent work of [11] and [6], where deep learning neuralnetworks have been interpreted as discretisations of an optimal control problemsubject to an ordinary differential equation constraint. We review the first orderconditions for optimality, and the conditions ensuring optimality after discretiza-tion. This leads to a class of algorithms for solving the discrete optimal controlproblem which guarantee that the corresponding discrete necessary conditions foroptimality are fulfilled. We discuss two different deep learning algorithms and makea preliminary analysis of the ability of the algorithms to generalise.nb_NO
dc.description.abstractDeep learning as optimal control problems: models and numerical methodsnb_NO
dc.language.isoengnb_NO
dc.publisherAmerican Institute of Mathematical Sciences (AIMS)nb_NO
dc.relation.urihttps://arxiv.org/pdf/1904.05657.pdf
dc.titleDeep learning as optimal control problems: models and numerical methodsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.journalJournal of Computational Dynamicsnb_NO
dc.identifier.doi10.3934/jcd.2019009
dc.identifier.cristin1697999
dc.relation.projectEC/H2020/CHiPSnb_NO
dc.relation.projectNorges forskningsråd: 231632nb_NO
dc.relation.projectEC/H2020/CHIPSnb_NO
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2019 by American Institute of Mathematical Sciencesnb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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