dc.date.accessioned | 2025-05-30T05:18:46Z | |
dc.date.available | 2025-05-30T05:18:46Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | http://publications.mfo.de/handle/mfo/4264 | |
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. | |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.title | Deep Learning for PDE-based Inverse Problems | |
dc.rights.license | Unless otherwise noted, the content of this report is licensed under Creative Commons Attribution-ShareAlike 4.0 International. | * |
dc.identifier.doi | 10.14760/OWR-2024-48 | |
local.series.id | OWR-2024-48 | |
local.subject.msc | 44 | |
local.subject.msc | 35 | |
local.subject.msc | 65 | |
local.date-range | 27 Oct - 01 Nov 2024 | |
local.workshopcode | 2444 | |
local.workshoptitle | Deep Learning for PDE-based Inverse Problems | |
local.organizers | Simon Arridge, London; Peter Maaß, Bremen; Carola-Bibiane Schönlieb, Cambridge UK | |
local.report-name | Workshop Report 2024,48 | |
local.opc-photo-id | 2444 | |
local.publishers-doi | 10.4171/OWR/2024/48 | |