Applications of Deep Learning in Electromagnetics : Teaching Maxwell's Equations to Machines

• Record Nr.: UNINA9911006703903321
• Autore: Li Maokun
• Pubbl/distr/stampa: Stevenage : , : Institution of Engineering & Technology, , 2023; ©2022
• ISBN: 1-83724-521-5; 1-5231-5536-1; 1-83953-590-3
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (378 pages)
• Collana: Electromagnetic Waves Series
• Altri autori (Persone): SalucciMarco
• Disciplina: 006.31
• Soggetti: Deep learning (Machine learning); Maxwell equations; Electromagnetism
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 -- 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&amp -- E modeling -- 4.2.1 Field solution for the flawless piece and calculation of the signal geometry ΔZTR(p) -- 4.2.2 Defect response: calculation of the flaw signal ΔZTR(d) -- 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&amp -- E -- 4.3.1 Most relevant deep learning architecture in NDT&amp -- E -- 4.4 Application of deep learning to electromagnetic NDT&amp -- E -- 4.4.1 Deep learning in electromagnetic NDT&amp -- 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&amp -- 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&amp -- 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.
• Sommario/riassunto: 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.