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.
Authors
- Peter Maaß (ORCID: https://orcid.org/0000-0003-1448-8345)
- Carola‐Bibiane Schönlieb (ORCID: https://orcid.org/0000-0003-0099-6306)
- Simon Arridge (ORCID: https://orcid.org/0000-0003-1292-0210)
Institutions
- University of Bremen (DE)
- University of Cambridge (GB)
- University College London (GB)
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
Funders
- National Science Foundation
- Universidade de Coimbra