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

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	October 2022
First published:	2022
Authors:	Qiang Ren • Yinpeng Wang • Yongzhong Li • Shutong Qi
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	125
Edition:	1st ed. 2022
ISBN-13:	978-981-16-6263-8
Categories:	Books > Science & Mathematics > Physics > Electricity, magnetism & electromagnetism Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Microwave technology Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	981-16-6263-0
Barcode:	9789811662638

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