Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

Herausgegeben:Campbell, Sawyer D.; Werner, Douglas H.

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• IEEE/OUP Series on Electromagnetic Wave Theory (formerly IEEE only), Series Editor: Donald G. Dudley.
• Verlag: Wiley & Sons / Wiley-IEEE Press
• Artikelnr. des Verlages: 1W119853890
• 1. Auflage
• Seitenzahl: 592
• Erscheinungstermin: 22. August 2023
• Englisch
• Abmessung: 260mm x 183mm x 36mm
• Gewicht: 1024g
• ISBN-13: 9781119853893
• ISBN-10: 1119853893
• Artikelnr.: 64106785

Autorenporträt

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.

Inhaltsangabe

About the Editors xix

List of Contributors xx

Preface xxvi

Section I Introduction to AI-Based Regression and Classification 1

1 Introduction to Neural Networks 3
Isha Garg and Kaushik Roy

1.1 Taxonomy 3

1.1.1 Supervised Versus Unsupervised Learning 3

1.1.2 Regression Versus Classification 4

1.1.3 Training, Validation, and Test Sets 4

1.2 Linear Regression 5

1.2.1 Objective Functions 6

1.2.2 Stochastic Gradient Descent 7

1.3 Logistic Classification 9

1.4 Regularization 11

1.5 Neural Networks 13

1.6 Convolutional Neural Networks 16

1.6.1 Convolutional Layers 17

1.6.2 Pooling Layers 18

1.6.3 Highway Connections 19

1.6.4 Recurrent Layers 19

1.7 Conclusion 20

References 20

2 Overview of Recent Advancements in Deep Learning and Artificial Intelligence 23
Vijaykrishnan Narayanan, Yu Cao, Priyadarshini Panda, Nagadastagiri Reddy Challapalle, Xiaocong Du, Youngeun Kim, Gokul Krishnan, Chonghan Lee, Yuhang Li, Jingbo Sun, Yeshwanth Venkatesha, Zhenyu Wang, and Yi Zheng

2.1 Deep Learning 24

2.1.1 Supervised Learning 26

2.1.1.1 Conventional Approaches 26

2.1.1.2 Deep Learning Approaches 29

2.1.2 Unsupervised Learning 35

2.1.2.1 Algorithm 35

2.1.3 Toolbox 37

2.2 Continual Learning 38

2.2.1 Background and Motivation 38

2.2.2 Definitions 38

2.2.3 Algorithm 38

2.2.3.1 Regularization 39

2.2.3.2 Dynamic Network 40

2.2.3.3 Parameter Isolation 40

2.2.4 Performance Evaluation Metric 41

2.2.5 Toolbox 41

2.3 Knowledge Graph Reasoning 42

2.3.1 Background 42

2.3.2 Definitions 42

2.3.3 Database 43

2.3.4 Applications 43

2.3.5 Toolbox 44

2.4 Transfer Learning 44

2.4.1 Background and Motivation 44

2.4.2 Definitions 44

2.4.3 Algorithm 45

2.4.4 Toolbox 46

2.5 Physics-Inspired Machine Learning Models 46

2.5.1 Background and Motivation 46

2.5.2 Algorithm 46

2.5.3 Applications 49

2.5.4 Toolbox 50

2.6 Distributed Learning 50

2.6.1 Introduction 50

2.6.2 Definitions 51

2.6.3 Methods 51

2.6.4 Toolbox 54

2.7 Robustness 54

2.7.1 Background and Motivation 54

2.7.2 Definitions 55

2.7.3 Methods 55

2.7.3.1 Training with Noisy Data/Labels 55

2.7.3.2 Adversarial Attacks 55

2.7.3.3 Defense Mechanisms 56

2.7.4 Toolbox 56

2.8 Interpretability 56

2.8.1 Background and Motivation 56

2.8.2 Definitions 57

2.8.3 Algorithm 57

2.8.4 ToolBox 58

2.9 Transformers and Attention Mechanisms for Text and Vision Models 58

2.9.1 Background and Motivation 58

2.9.2 Algorithm 59

2.9.3 Application 60

2.9.4 Toolbox 61

2.10 Hardware for Machine Learning Applications 62

2.10.1 Cpu 62

2.10.2 Gpu 63

2.10.3 ASICs 63

2.10.4 Fpga 64

Acknowledgment 64

References 64

Section II Advancing Electromagnetic Inverse Design with Machine Learning 81

3 Breaking the Curse of Dimensionality in Electromagnetics Design Through Optimization Empowered by Machine Learning 83
N. Anselmi, G. Oliveri, L. Poli, A. Polo, P. Rocca, M. Salucci, and A. Massa

3.1 Introduction 83

3.2 The SbD Pillars