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Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts
Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics.
To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include:
Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.
Sawyer D. Campbell is an Assistant Research Professor in the Pennsylvania State University
Department of Electrical Engineering where he is also the associate director of the Computational Electromagnetics and Antennas Research Lab.
Douglas H. Werner is the director of the Computational Electromagnetics and Antennas Research Lab as well as a faculty member of the Materials Research Institute at Penn State.
1.Definitions and Basics of Deep Learning and Artificial Intelligence
2.Overview of Recent Advancements in DL and AI
3.Breaking the Curse of Dimensionality in Advances in Electromagnetics Design Through Optimization Empowered by Machine Learning
4.Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization
5.Advanced Neural Networks for Electromagnetic Modeling and Design
6.Generative Machine Learning for Photonic Design
7.Inverse Design of Electromagnetic Systems Using Deep Generative Neural Networks
8.Exhaustive Characterization of Metasurface Supercell Robustness Using Deep Learning
9.Machine Learning in Metasurfaces Design and Their Applications
10.Deep Learning for Metasurfaces and Metasurfaces for Deep Learning
11.Forward and Inverse Design of Artificial Electromagnetic Materials
12.Machine Learning-Assisted Optimization and its Application to Antenna and Array Designs
13.Analysis of Uniform and Non-Uniform Antenna Arrays Using Kernel Methods
14.Knowledge-based Globalized Optimization of High-frequency Structures Using Inverse Surrogates
15.Deep Learning for High Contrast Inverse Scattering of Electrically Large Structures
16.Radar Target Classification Using Deep Learning
17.Koopman Autoencoder for Physics-informed Machine Learning Based Reduced-order Kinetic Plasma Models
Index
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.
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