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Deep Learning for PDE-based Inverse Problems

dc.date.accessioned2025-05-30T05:18:46Z
dc.date.available2025-05-30T05:18:46Z
dc.date.issued2024
dc.identifier.urihttp://publications.mfo.de/handle/mfo/4264
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.
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleDeep Learning for PDE-based Inverse Problems
dc.rights.licenseUnless otherwise noted, the content of this report is licensed under Creative Commons Attribution-ShareAlike 4.0 International.*
dc.identifier.doi10.14760/OWR-2024-48
local.series.idOWR-2024-48
local.subject.msc44
local.subject.msc35
local.subject.msc65
local.date-range27 Oct - 01 Nov 2024
local.workshopcode2444
local.workshoptitleDeep Learning for PDE-based Inverse Problems
local.organizersSimon Arridge, London; Peter Maaß, Bremen; Carola-Bibiane Schönlieb, Cambridge UK
local.report-nameWorkshop Report 2024,48
local.opc-photo-id2444
local.publishers-doi10.4171/OWR/2024/48


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