Deep Learning for PDE-based Inverse Problems

dc.bibliographicCitation.firstPage2805
dc.bibliographicCitation.issue4
dc.bibliographicCitation.journalTitleOberwolfach reports : OWR
dc.bibliographicCitation.lastPage2900
dc.bibliographicCitation.volume21
dc.contributor.otherArridge, Simon
dc.contributor.otherMaaß, Peter
dc.contributor.otherSchönlieb, Carola-Bibiane
dc.date.accessioned2026-03-19T10:33:57Z
dc.date.available2026-03-19T10:33:57Z
dc.date.issued2024
dc.description.abstractAnalysing learned concepts for PDE-based parameter identification problems requires input from different research areas such as inverse problems, partial differential equations, statistics and mathematical foundations of deep learning. This workshop brought together a critical mass of experts in the various field. A thorough mathematical theory for PDE-based inverse problems using learned concepts is within reach in the coming few years and the inspiration of this Oberwolfach meeting will substantially influence this development.eng
dc.description.versionpublishedVersion
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/32975
dc.identifier.urihttps://doi.org/10.34657/32044
dc.language.isoeng
dc.publisherZürich : EMS Publ. House
dc.relation.doihttps://doi.org/10.4171/OWR/2024/48
dc.relation.essn1660-8941
dc.relation.issn1660-8933
dc.rights.licenseCC BY-SA 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subject.ddc510
dc.subject.gndKonferenzschriftger
dc.titleDeep Learning for PDE-based Inverse Problemseng
dc.typeArticle
tib.accessRightsopenAccess
wgl.contributorMFO
wgl.subjectMathematik
wgl.typeZeitschriftenartikel

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