Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines

Cover
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
3.5.2 Application in electrical impedance and capacitance tomography
3.6 Learning-augmented iterative methods
3.7 Non-iterative learning methods
3.8 Closing remarks
References
4 Deep learning techniques for non-destructive testing and evaluation
4.1 Introduction
4.2 Principles of electromagnetic NDT&E modeling
4.2.1 Field solution for the flawless piece and calculationof the signal geometry Z ( p) TR
4.2.2 Defect response: calculation of the flaw signal Z ( d) TR
4.2.3 Examples
4.2.4 Inverse problems by means of optimization and machine learning approaches
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
5.5 Deep learning in seismic data inversion
5.5.1 Inversion with unsupervised RNN
5.5.2 Low-frequency data prediction
5.5.3 Physically realistic dataset construction
5.5.4 Learning the optimization
5.5.5 Deep learning constrained traveltime tomography
5.6 Deep learning in multi-physics joint inversion
5.7 Construction of the training dataset
5.8 Conclusions and outlooks
References
6 Deep learning techniques for biomedical imaging
6.1 Introduction
6.2 Physics of medical imaging
6.2.1 Maxwell's equations
6.2.2 Formulations of EIT
6.2.3 Formulations of MWI
6.2.4 Inverse methods for EIT and MWI
6.3 Deep-learning in medical imaging
6.3.1 Machine learning
6.3.2 Deep learning neural networks
6.3.3 DNN in medical imaging
6.4 Hybrid physics-based learning-assisted medical imaging: example studies
6.4.1 Example 1: EIT-based SDL-assisted imaging
6.4.2 Example 2: MWI(CSI)-based UNet-assisted imaging
6.4.3 Example 3: MWI(BIM)-based CNN-assisted imaging
6.5 Summary
References
7 Deep learning techniques for direction of arrival estimation
7.1 Introduction
7.2 Problem formulation
7.2.1 Conventional observation model
7.2.2 Overcomplete formulation of array outputs
7.2.3 Array imperfections
7.3 Deep learning framework for DOA estimation
7.3.1 Data pre-processing
7.3.2 Deep learning model
7.3.3 Post-processing for DOA refinement
7.4 A hybrid DNN architecture for DOA estimation
7.4.1 The hierarchical DNN structure
7.4.2 Training strategy of the hybrid DNN model
7.4.3 Simulations and analyses
7.5 Concluding remarks and future trends
References
8 Deep learning techniques for remote sensing
8.1 Target recognition
8.1.1 Ship detection
8.1.2 Aircraft recognition
8.1.3 Footprint extraction
8.1.4 Few-shot recognition of SAR targets
8.2 Land use and land classification
8.2.1 Local climate zone classification
8.2.2 Crop-type classification
8.2.3 SAR-optical fusion for land segmentation
8.3 Disaster monitoring
8.3.1 Flood detection
8.3.2 Storm nowcasting
8.3.3 Lightning nowcasting
8.4 Forest applications
8.4.1 Tree species classification
8.4.2 Deforestation mapping
8.4.3 Fire monitoring
8.5 Conclusions
References
9 Deep learning techniques for digital satellite communications
9.1 Introduction
9.2 Machine learning for SatCom
9.2.1 Deep learning
9.3 Digital satellite communication systems
9.3.1 Uplink segment
9.3.2 Space segment
9.3.3 Downlink segment
9.4 SatCom systems modelling
9.4.1 High-power amplifier modelling
9.5 SNR estimation
9.5.1 Autoencoders
9.5.2 SNR estimation methodology
9.5.3 Metrics
9.5.4 Application example
9.5.5 Metrics tuning and consistency analysis
9.5.6 Results and discussion
9.6 Input back-off estimation
9.6.1 Deep learning model for IBO estimation
9.6.2 Performance metric
9.6.3 Data generation
9.6.4 Results and discussion
9.7 Conclusion
References
10 Deep learning techniques for imaging and gesture recognition
10.1 Introduction
10.2 Design of reprogrammable metasurface
10.3 Intelligent metasurface imager
10.3.1 System configuration
10.3.2 Results
10.4 VAE-based intelligent integrated metasurface sensor
10.4.1 System configuration
10.4.2 Variational auto-encoder (VAE) principle
10.4.3 Results
10.5 Free-energy-based intelligent integrated metasurface sensor
10.5.1 System configuration
10.5.2 Free-energy minimization principle
10.5.3 Results
References
11 Deep learning techniques for metamaterials and metasurfaces
design
11.1 Introduction
11.2 Discriminative learning approach
11.3 Generative learning approach
11.4 Reinforcement learning approach
11.5 Deep learning and optimization hybrid approach
11.6 Summary
References
12 Deep learning techniques for microwave circuit modeling
12.1 Introduction
12.2 Feedforward deep neural network for microwave circuit modeling
12.2.1 Feedforward deep neural network and the vanishing gradient problem
12.2.2 A hybrid feedforward deep neural network
12.3 Recurrent neural networks for microwave circuit modeling
12.3.1 Global-feedback recurrent neural network
12.3.2 Adjoint recurrent neural network
12.3.3 Global-feedback deep recurrent neural network
12.3.4 Local-feedback deep recurrent neural network
12.3.5 Long short-term memory neural network
12.4 Application examples of deep neural network for microwave modeling
12.4.1 High-dimensional parameter-extraction modeling using the hybrid feedforward deep neural network
12.4.2 Macromodeling of audio amplifier using long short-term memory neural network
12.5 Discussion
12.6 Conclusion
References
13 Concluding remarks, open challenges, and future trends
13.1 Introduction
13.2 Pros and cons of DL
13.2.1 High computational efficiency and accuracy
13.2.2 Bypassing feature engineering
13.2.3 Large amounts of training data
13.2.4 High computational burden
13.2.5 Deep architectures, not learning
13.2.6 Lack of transparency
13.3 Open challenges
13.3.1 The need for less data and higher efficiency
13.3.2 Handling data outside the training distribution
13.3.3 Improving flexibility and enabling multi-tasking
13.3.4 Counteracting over-fitting
13.4 Future trends
13.4.1 Few shot, one shot, and zero shot learning
13.4.2 Foundation models
13.4.3 Attention schemes and transformers
13.4.4 Deep symbolic reinforcement learning
13.5 Conclusions
References
Index
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