Deep Learning for PDE-based Inverse Problems
| dc.bibliographicCitation.firstPage | 2805 | |
| dc.bibliographicCitation.issue | 4 | |
| dc.bibliographicCitation.journalTitle | Oberwolfach reports : OWR | |
| dc.bibliographicCitation.lastPage | 2900 | |
| dc.bibliographicCitation.volume | 21 | |
| dc.contributor.other | Arridge, Simon | |
| dc.contributor.other | Maaß, Peter | |
| dc.contributor.other | Schönlieb, Carola-Bibiane | |
| dc.date.accessioned | 2026-03-19T10:33:57Z | |
| dc.date.available | 2026-03-19T10:33:57Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Analysing 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.version | publishedVersion | |
| dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/32975 | |
| dc.identifier.uri | https://doi.org/10.34657/32044 | |
| dc.language.iso | eng | |
| dc.publisher | Zürich : EMS Publ. House | |
| dc.relation.doi | https://doi.org/10.4171/OWR/2024/48 | |
| dc.relation.essn | 1660-8941 | |
| dc.relation.issn | 1660-8933 | |
| dc.rights.license | CC BY-SA 4.0 Unported | |
| dc.rights.uri | https://creativecommons.org/licenses/by-sa/4.0/ | |
| dc.subject.ddc | 510 | |
| dc.subject.gnd | Konferenzschrift | ger |
| dc.title | Deep Learning for PDE-based Inverse Problems | eng |
| dc.type | Article | |
| tib.accessRights | openAccess | |
| wgl.contributor | MFO | |
| wgl.subject | Mathematik | |
| wgl.type | Zeitschriftenartikel |
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