The DOI matches its Crossref record; 342 of the 342 references it cites were found in a registry or an index.
Solving inverse problems using data-driven models
Open accessAbstract
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.
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Cites
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Image quality assessment: from error visibility to structural similarity
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Regression shrinkage and selection via the lasso
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Nonlinear total variation based noise removal algorithms
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Metadata
- Authors
- Arridge, S., Maass, P., Öktem, O., & Schönlieb, C.-B.
- Venue
- Acta Numerica
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-05-01
- Volume
- 28
- Pages
- 1-174
- ISSN
- 0962-4929 (ISSN-L)
- 1474-0508
- OpenAlex
- W2952020389
- Cited by
- 686
- References
- 342
- FWCI
- 92.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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