Applications of Deep Learning in Electromagnetics Teaching Maxwell’s equations to machines 1st edition by Maokun Li, Marco Salucci 183953589X 978-1839535895

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ISBN-10 ‏ : ‎ 183953589X

ISBN-13 ‏ : ‎ 978-1839535895

Author: Maokun Li, Marco Salucci

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.

 

Applications of Deep Learning in Electromagnetics: Teaching Maxwell’s equations to machines 1st Table of contents:

An introduction to deep learning for electromagnetics

Deep learning techniques for electromagnetic forward modeling

Deep learning techniques for free-space inverse scattering

Deep learning techniques for non-destructive testing and evaluation

Deep learning techniques for subsurface imaging

Deep learning techniques for biomedical imaging

Deep learning techniques for direction of arrival estimation

Deep learning techniques for remote sensing

Deep learning techniques for digital satellite communications

Deep learning techniques for imaging and gesture recognition

 

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Tags: Applications, Deep Learning, Electromagnetics, Teaching Maxwell, equations, Maokun Li, Marco Salucci