Expected by: 01 June 2023
Applications of Deep Learning in Electromagnetics

Applications of Deep Learning in Electromagnetics

Teaching Maxwell's equations to machines  

Edited by 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.

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.

About the Editors

Maokun Li is an associate professor in the Department of Electronic Engineering at Tsinghua University, Beijing, China. He received his BS degree in electronic engineering from Tsinghua University, Beijing, China, in 2002, and his MS and PhD degrees in electrical engineering from University of Illinois at Urbana-Champaign in 2004 and 2007, respectively. After graduation, he worked in Schlumberger-Doll Research as a research scientist before he joined Tsinghua University in 2014.

Marco Salucci is an assistant professor at the DICAM Department at the University of Trento, and a research fellow of the ELEDIA Research Center. He received his PhD from the University of Trento in 2014. He was previously a postdoctoral researcher at Centrale Supélec and then at the Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), in France.

Publication Year: 2023

Pages: 400

ISBN-13: 978-1-83953-589-5

Format: HBK

Editors: Maokun Li, Marco Salucci

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