Journal
ACTA NUMERICA
Volume 28, Issue -, Pages 1-174Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0962492919000059
Keywords
-
Categories
Funding
- EPSRC [EP/N014588/1, EP/M020533/1, EP/M00483X/1]
- Swedish Foundation of Strategic Research grant [AM13-004]
- Alan Turing Institute
- Leverhulme Trust project 'Breaking the non-convexity barrier'
- RISE project CHiPS
- RISE project NoMADS
- Cantab Capital Institute for the Mathematics of Information
- EPSRC [EP/M00483X/1, EP/N014588/1, EP/S026045/1, EP/M020533/1, EP/J009539/1, EP/N022750/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M020533/1] Funding Source: researchfish
Ask authors/readers for more resources
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical-analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
