Li M. Applications of Deep Learning in Electromagnetics...2023 torrent

Textbook in PDF format

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.
With the help of big data, massive parallelization, and computational algorithms, Deep Learning (DL) techniques have been developed rapidly during the recent years. Complex artificial neural networks, trained by large amounts of data, have demonstrated unprecedented performance in many tasks in artificial intelligence, such as image and speech recognition. This success also leads DL into many other fields of engineering. And electromagnetics (EM) is one of them.
This book is intended to overview the recent research progresses in applying DL techniques in EM engineering. Traditionally, research and development in this field have been always based on EM theory. The EM field distribution in engineering problems is modeled and solved by means of Maxwell’s equations. The results can be very accurate, especially with the help of modern computational tools. However, when the system gets more complex, it is tough to solve because the increase in the degree-of-freedom exceeds the modeling and computational capabilities. Meanwhile, the demand for real-time computing also poses a significant challenge in the current EM modeling procedure.
DL can be used to alleviate some of the above challenges. First, it can “learn” from measured data and master some information about the complex scenarios for the solution procedure, which can improve the accuracy of modeling and data processing. Second, it can reduce the computational complexity in EM modeling by building fast surrogate models. Third, it can discover new designs and accelerate the design process while combining with other design tools. More engineering applications are being investigated with deep learning techniques, such as antenna design, circuit modeling, EM sensing and imaging, etc. The contents of the book are as follows:
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.
Deep learning techniques for metamaterials and metasurfaces design.
Deep learning techniques for microwave circuit modeling.
Concluding remarks, open challenges, and future trends