Yinpeng Wang, Qiang Ren

Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 194
• Erscheinungstermin: 6. Juli 2023
• Englisch
• ISBN-13: 9781000896671
• Artikelnr.: 68134746

Autorenporträt

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.

Inhaltsangabe

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