| Description |
1 online resource (630 p.) |
| Series |
Electromagnetic Waves Series |
|
Electromagnetic Waves Series
|
| Contents |
Intro -- Title -- Copyright -- Contents -- About the editors -- Foreword -- Acknowledgment -- 1 An introduction to deep learning for electromagnetics -- 1.1 Introduction -- 1.2 Basic concepts and taxonomy -- 1.2.1 What is deep learning? -- 1.2.2 Classification of deep learning techniques -- 1.3 Popular DL architectures -- 1.3.1 Convolutional neural networks -- 1.3.2 Recurrent neural networks -- 1.3.3 Generative adversarial networks -- 1.3.4 Autoencoders -- 1.4 Conclusions -- Acknowledgments -- References -- 2 Deep learning techniques for electromagnetic forward modeling -- 2.1 Introduction |
|
2.2 DL and ordinary/partial differential equations -- 2.3 Fully data-driven forward modeling -- 2.4 DL-assisted forward modeling -- 2.5 Physics-inspired forward modeling -- 2.6 Summary and outlook -- References -- 3 Deep learning techniques for free-space inverse scattering -- 3.1 Inverse scattering challenges -- 3.2 Traditional approaches -- 3.2.1 Traditional approximate solutions -- 3.2.2 Traditional iterative methods -- 3.3 Artificial neural networks applied to inverse scattering -- 3.4 Shallow network architectures -- 3.5 Black-box approaches -- 3.5.1 Approaches for phaseless data |
|
4.3 Applications of deep learning approaches for forward and inverse problems in NDT&E -- 4.3.1 Most relevant deep learning architecture in NDT&E -- 4.4 Application of deep learning to electromagnetic NDT&E -- 4.4.1 Deep learning in electromagnetic NDT&E applied to the energy sector -- 4.4.2 Applications to the transportation and civil infrastructures sectors -- 4.4.3 Applications to the manufacturing and agri-food sectors -- 4.5 Applications to higher frequency NDT&E methods -- 4.5.1 Infrared thermography testing and terahertz wave testing -- 4.5.2 Radiographic testing |
|
4.6 Future trends and open issues for deep learning algorithms as applied to electromagnetic NDT&E -- 4.7 Conclusion and remarks -- 4.8 Acknowledgments -- References -- 5 Deep learning techniques for subsurface imaging -- 5.1 Introduction -- 5.2 Purely data-driven approach -- 5.2.1 Convolutional neural network -- 5.2.2 Recurrent neural network -- 5.2.3 Generative adversarial network -- 5.3 Physics embedded data-driven approach -- 5.3.1 Supervised descent method -- 5.3.2 Physics embedded deep neural network -- 5.4 Learning-assisted physics-driven approach |
| Summary |
This book discusses recent advances in the application of deep learning techniques to electromagnetic theory and engineering. The contents represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate |
| Notes |
Description based upon print version of record |
|
5.5 Deep learning in seismic data inversion |
| Subject |
Maxwell equations.
|
|
Electromagnetism.
|
|
Deep learning (Machine learning)
|
| Form |
Electronic book
|
| Author |
Li, Maokun
|
|
Salucci, Marco.
|
| ISBN |
1839535903 |
|
9781839535901 |
|
