Data-driven Models in Inverse Problems
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Beschreibung
Advances in learning-based methods are revolutionizing several fields in applied mathematics, including inverse problems, resulting in a major paradigm shift towards data-driven approaches. This volume, which is inspired by this cutting-edge area of research, brings together contributors from the inverse problem community and shows how to successfully combine model- and data-driven approaches to gain insight into practical and theoretical issues.
Autor*in
Tatiana A. Bubba
Themen in »Data-driven Models in Inverse Problems«
Tiefe Neuronale Netze Regularisierung inverser Probleme Computergestützte Bildgebung Numerische Methoden für inverse Probleme Signalverarbeitung Deep neural networks Regularization of inverse problems Computerized imaging Numerical methods for inverse problems Signal processing