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Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning, 1st ed. 2022

Langue : Anglais

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Couverture de l’ouvrage Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning
This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
Introduction to Electromagnetic Problems.- Basic Principles of Unveiling Electromagnetic Problems Based on Deep Learning.- Building Database.- Two-Dimensional Electromagnetic Scattering Solver.- Three-Dimensional Electromagnetic Scattering Solver.

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 Sept 2017, he joined the School of Electronics and Information Engineering, Beihang University, Beijing, China, as an “Excellent Hundred” Associate Professor.

Dr. Ren is the recipient of the Young Scientist Award of 2018 International Applied Computational Electromagnetics Society (ACES) Symposium in China. He serves as the Associate Editor of ACES Journal and Microwave and Optical Technology Letters (MOTL), and also serves as a reviewer for more than 30 journals. His current research interests include numerical modeling methods for complex media, multiscale and multiphysics problems, inverse scattering, deep learning and parallel computing. He has published more than 70 papers on the journals and conferences.

 

Yinpeng Wang received the B.S. degree in Electronic and Information Engineering from Beihang University in 2020, where he is currently pursuing his M.S. degree in Electronic Science and Technology. From 2017 to 2018, he was a researcher at the Physical Experiment Center, Beihang University. In 2018, he worked as a research assistant in the Spintronics Interdisciplinary Center. Since 2018, he has been a member of the Institute of EMC Technology.

Mr. Wang focuses on the research of electromagnetic scattering, inverse scattering, heat transfer, computational multi-physical fields, and deep learning. Since 2018, Mr. Wang has published 8 peer-reviewed technical papers in international journals and conferences. He serves as a reviewer f

Introduces a complete workflow in constructing a deep learning framework to predict the scattering fields Provides a guidance of building and validating the 2-D and 3-D electromagnetics dataset Provides a pre-experimental basis for the selection of network hyperparameters, split ratios and activation functions

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Date de parution :

Ouvrage de 125 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

137,14 €

Ajouter au panier