Data-driven Models in Inverse Problems

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.

Product details

• Assisted by: Tatiana A Bubba (Editor), Tatiana A. Bubba (Editor)
• Publisher: De Gruyter
• Languages: English
• Product format: Hardback
• Released: 18.11.2024
• EAN: 9783111250038
• ISBN: 978-3-11-125003-8
• No. of pages: 500
• Weight: 965 g
• Illustrations: 36 b/w and 80 col. ill.
• Series: Radon Series on Computational and Applied Mathematics
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Künstliche Intelligenz, Artificial Intelligence, Signalverarbeitung, Bildverarbeitung, Deep Neural Networks, Signal Processing, Applied mathematics, Databases, computerized imaging, COM018000 COMPUTERS / Data Processing, COM004000 COMPUTERS / Intelligence (AI) & Semantics, COM021030 COMPUTERS / Database Management / Data Mining, Regularisierung inverser Probleme, Numerical methods for inverse problems, Numerische Methoden für inverse Probleme, Tiefe Neuronale Netze, Regularization of inverse problems, Computergestützte Bildgebung