Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning - Ren, Qiang

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Résumé :
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.

Biographie:

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 for Springer, IOP and IEEE journals. He was a recipient of the Outstanding Freshmen Scholarship and the First Prize of the Academic Scholarship.

Mr. Li focus on the research of numerical modeling for multiphysics and multiscale problem, machine learning and wave functional material. Since 2018, Mr. Li has published 6 peer-reviewed technical papers in international journals and conferences. He serves as a Reviewer for IOP journals. He was a recipient of the Outstanding Graduate in 2020.

Shutong Qi received the B.S. degree in Electronic and Information Engineering from Beihang University in 2020. He is pursing the Ph.D. degree in Electrical and Computer Engineering, University of Toronto. From 2018 to 2020, he has been a research assistant of the Institute of EMC Technology. Now he is a graduate student of University of Toronto.

Mr. Qi focus on the research of finite-difference method, electromagnetic scattering, deep learning and human-computer interaction. Since 2018, Mr. Qi has published 7 peer-reviewed technical papers in international journals and conferences. He was a recipient of the Outstanding Graduate in 2020.