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Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning

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Springer


Paru le : 2021-10-20

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Description
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.
Pages
125 pages
Collection
n.c
Parution
2021-10-20
Marque
Springer
EAN papier
9789811662607
EAN PDF
9789811662614
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
5161 Ko
Prix
126,59 €
EAN EPUB
9789811662614
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
28201 Ko
Prix
126,59 €

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