Deep Learning for PDE-based Inverse Problems

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.

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Publication Details

Journal
Oberwolfach Reports
Published
2025-05-19
DOI
https://doi.org/10.4171/owr/2024/48
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24

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article

Deep Learning for PDE-based Inverse Problems

Peter Maaß, Carola‐Bibiane Schönlieb, Simon Arridge
1 citations
Oberwolfach Reports
Industrial Vision Systems and Defect Detection
1.24
article

Deep Learning for PDE-based Inverse Problems

Peter Maaß, Carola‐Bibiane Schönlieb, Simon Arridge
article en
1 citations

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.

Oberwolfach ReportsVol. 21(4)
University of Bremen (DE), University of Cambridge (GB), University College London (GB)
National Science Foundation, Universidade de Coimbra
Openalex Percentile: Top 19%
Industrial Vision Systems and Defect Detection
1.24
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