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[null,"1.","Record Nr.",null,"UNINA9911006703903321"]
[null,null,"Autore",null,"Li Maokun"]
[null,null,"Titolo",null,"Applications of Deep Learning in Electromagnetics : Teaching Maxwell's Equations to Machines"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2023"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-83724-521-5"]
[{"$nulls":4},"1-5231-5536-1"]
[{"$nulls":4},"1-83953-590-3"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (378 pages)"]
[null,null,"Collana",null,"Electromagnetic Waves Series"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Deep learning (Machine learning)"]
[{"$nulls":4},"Maxwell equations"]
[{"$nulls":4},"Electromagnetism"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Title -- Copyright -- Contents -- About the editors -- Foreword -- Acknowledgment -- 1 An introduction to deep learning for electromagnetics -- 1.1 Introduction -- 1.2 Basic concepts and taxonomy -- 1.2.1 What is deep learning? -- 1.2.2 Classification of deep learning techniques -- 1.3 Popular DL architectures -- 1.3.1 Convolutional neural networks -- 1.3.2 Recurrent neural networks -- 1.3.3 Generative adversarial networks -- 1.3.4 Autoencoders -- 1.4 Conclusions -- Acknowledgments -- References -- 2 Deep learning techniques for electromagnetic forward modeling -- 2.1 Introduction -- 2.2 DL and ordinary/partial differential equations -- 2.3 Fully data-driven forward modeling -- 2.4 DL-assisted forward modeling -- 2.5 Physics-inspired forward modeling -- 2.6 Summary and outlook -- References -- 3 Deep learning techniques for free-space inverse scattering -- 3.1 Inverse scattering challenges -- 3.2 Traditional approaches -- 3.2.1 Traditional approximate solutions -- 3.2.2 Traditional iterative methods -- 3.3 Artificial neural networks applied to inverse scattering -- 3.4 Shallow network architectures -- 3.5 Black-box approaches -- 3.5.1 Approaches for phaseless data -- 3.5.2 Application in electrical impedance and capacitance tomography -- 3.6 Learning-augmented iterative methods -- 3.7 Non-iterative learning methods -- 3.8 Closing remarks -- References -- 4 Deep learning "]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.3 Applications of deep learning approaches for forward and inverse problems in NDT&amp -- E -- 4.3.1 Most relevant deep learning architecture in NDT&amp -- E -- 4.4 Application of deep learning to electromagnetic NDT&amp -- E -- 4.4.1 Deep learning in electromagnetic NDT&amp -- E applied to the energy sector -- 4.4.2 Applications to the transportation and civil infrastructures sectors -- 4.4.3 Applications to the manufacturing and agri-food sectors -- 4.5 Applications to higher frequency NDT&amp -- E methods -- 4.5.1 Infrared thermography testing and terahertz wave testing -- 4.5.2 Radiographic testing -- 4.6 Future trends and open issues for deep learning algorithms as applied to electromagnetic NDT&amp -- E -- 4.7 Conclusion and remarks -- 4.8 Acknowledgments -- References -- 5 Deep learning techniques for subsurface imaging -- 5.1 Introduction -- 5.2 Purely data-driven approach -- 5.2.1 Convolutional neural network -- 5.2.2 Recurrent neural network -- 5.2.3 Generative adversarial network -- 5.3 Physics embedded data-driven approach -- 5.3.1 Supervised descent method -- 5.3.2 Physics embedded deep neural network -- 5.4 Learning-assisted physics-driven approach -- 5.5 Deep learning in seismic data inversion -- 5.5.1 Inversion with unsupervised RNN -- 5.5.2 Low-frequency data prediction -- 5.5.3 Physically realistic dataset construction -- 5.5.4 Learning the optimization -- 5.5.5 Deep learning constrained traveltime tomography -- 5.6 Deep learning in multi-physics joint inversion -- 5.7 Construction of the training dataset -- 5.8 Conclusions and outlooks -- References -- 6 Deep learning techniques for biomedical imaging -- 6.1 Introduction -- 6.2 Physics of medical imaging -- 6.2.1 Maxwell's equations -- 6.2.2 Formulations of EIT -- 6.2.3 Formulations of MWI -- 6.2.4 Inverse methods for EIT and MWI -- 6.3 Deep-learning in medical imaging."]
[{"$nulls":4},"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 -- "]
[{"$nulls":4},"8.4.3 Fire monitoring -- 8.5 Conclusions -- References -- 9 Deep learning techniques for digital satellite communications -- 9.1 Introduction -- 9.2 Machine learning for SatCom -- 9.2.1 Deep learning -- 9.3 Digital satellite communication systems -- 9.3.1 Uplink segment -- 9.3.2 Space segment -- 9.3.3 Downlink segment."]
[{"$nulls":4},"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."]
[{"$nulls":4},"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."]
[null,null,null,"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."]