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Sophisticated electromagnetic forwar...

Ren, Qiang.

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Sophisticated electromagnetic forward scattering solver via deep learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sophisticated electromagnetic forward scattering solver via deep learning/ by Qiang Ren ... [et al.].
other author:	Ren, Qiang.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xviii, 125 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Electromagnetic waves - Scattering -
Online resource:	https://doi.org/10.1007/978-981-16-6261-4
ISBN:	9789811662614

Sophisticated electromagnetic forward scattering solver via deep learning
Sophisticated electromagnetic forward scattering solver via deep learning[electronic resource] /by Qiang Ren ... [et al.]. - Singapore :Springer Singapore :2022. - xviii, 125 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811662614

Standard No.: 10.1007/978-981-16-6261-4doiSubjects--Topical Terms:

3590278
Electromagnetic waves
--Scattering

LC Class. No.: QC665.S3

Dewey Class. No.: 539.2

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300 $a xviii, 125 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Electromagnetic waves $x Scattering $x Data processing. $3 3590278
650 0 $a Machine learning. $3 533906
650 1 4 $a Microwaves, RF and Optical Engineering. $3 893985
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650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
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650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Ren, Qiang. $3 2131599
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