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Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines

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Editors: Maokun Li 1 ; Marco Salucci 2
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Publication Year: 2022

Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation.

Electromagnetic applications of deep learning covered in the book include electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface imaging, biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, as well as microwave circuit modeling.

Applications of Deep Learning in Electromagnetics contains valuable information for researchers looking for new tools to solve Maxwell's equations, students of electromagnetic theory, and researchers in the field of deep learning with an interest in novel applications.

Inspec keywords: remote sensing; deep learning (artificial intelligence); computational electromagnetics

Other keywords: deep learning (artificial intelligence); learning (artificial intelligence); gesture recognition; artificial intelligence; remote sensing; neural nets; data acquisition; data compression; computational electromagnetics; geophysical image processing

Subjects: Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Electrical engineering computing; Education and training; Optical, image and video signal processing; General electrical engineering topics; Neural nets; General and management topics

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