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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems de Yinpeng Wang

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

Autor Yinpeng Wang, Qiang Ren
en Limba Engleză Paperback – 30 ian 2025
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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Specificații

ISBN-13: 9781032503035
ISBN-10: 1032503033
Pagini: 200
Dimensiuni: 156 x 234 x 11 mm
Greutate: 0.31 kg
Editura: CRC Press

Cuprins

1. Deep Learning Framework and Paradigm in Computational Physics   2. Application of U-net in 3D Steady Heat Conduction Solver  3. Inversion of complex surface heat flux based on ConvLSTM  4. Time-domain electromagnetic inverse scattering based on deep learning  5. Reconstruction of thermophysical parameters based on deep learning  6. Advanced Deep Learning Techniques in Computational Physics 

Notă biografică

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.