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  • Produktbild: Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning
  • Produktbild: Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

Aus der Reihe IEEE Press Series on Electromagnetic Wave Theory and Applications

183,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.08.2023

Herausgeber

Sawyer D. Campbell + weitere

Verlag

John Wiley & Sons Inc

Seitenzahl

592

Maße (L/B/H)

26/18,3/3,6 cm

Gewicht

1299 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-85389-3

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.08.2023

Herausgeber

Verlag

John Wiley & Sons Inc

Seitenzahl

592

Maße (L/B/H)

26/18,3/3,6 cm

Gewicht

1299 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-85389-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning
  • Produktbild: Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

  • 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 and Fundamental Concepts 85

    3.3 SbD at Work in EMs Design 88

    3.3.1 Design of Elementary Radiators 88

    3.3.2 Design of Reflectarrays 92

    3.3.3 Design of Metamaterial Lenses 93

    3.3.4 Other SbD Customizations 96

    3.4 Final Remarks and Envisaged Trends 101

    Acknowledgments 101

    References 102

    4 Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization 105
    Feng