Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

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:

• Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems
• RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays
• Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures

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

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