Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems - Hardcover

Wang, Yinpeng; Ren, Qiang

 
9781032502984: Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Hardcover
ISBN 10: 1032502983 ISBN 13: 9781032502984
Verlag: CRC Press, 2023

Inhaltsangabe

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.

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Über die Autorin bzw. den Autor

Yinpeng Wang received the B.S. degree in Electronic and Information Engineering from Beihang University, Beijing, China in 2020, where he is currently pursuing his M.S. degree in Electronic Science and Technology. Mr. Wang focuses on the research of electromagnetic scattering, inverse scattering, heat transfer, computational multi-physical fields, and deep learning.

Qiang Ren received the B.S. and M.S. degrees both in electrical engineering from Beihang University, Beijing, China, and Institute of Acoustics, Chinese Academy of Sciences, Beijing, China in 2008 and 2011, respectively, and the PhD degree in Electrical Engineering from Duke University, Durham, NC, in 2015. From 2016 to 2017, he was a postdoctoral researcher with the Computational Electromagnetics and Antennas Research Laboratory (CEARL) of the Pennsylvania State University, University Park, PA. In September 2017, he joined the School of Electronics and Information Engineering, Beihang University as an "Excellent Hundred" Associate Professor.

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Verlag
CRC Press
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
1032502983
ISBN 13 
9781032502984
Einband
Tapa dura
Auflage
1
Anzahl der Seiten
200
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar

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9781032503035: Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems

Vorgestellte Ausgabe

ISBN 10:  1032503033 ISBN 13:  9781032503035
Verlag: CRC Press, 2025
Softcover