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Applications Of Deep Learning In Electromagnetics
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Book Synopsis Applications of Deep Learning in Electromagnetics by : Maokun Li
Download or read book Applications of Deep Learning in Electromagnetics written by Maokun Li and published by IET. This book was released on 2023-04-13 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning by : Sawyer D. Campbell
Download or read book Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning written by Sawyer D. Campbell and published by John Wiley & Sons. This book was released on 2023-09-26 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.
Book Synopsis Machine Learning Applications in Electromagnetics and Antenna Array Processing by : Manel Martínez-Ramón
Download or read book Machine Learning Applications in Electromagnetics and Antenna Array Processing written by Manel Martínez-Ramón and published by Artech House. This book was released on 2021-04-30 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.
Book Synopsis The Application of Machine Learning for Designing and Controlling Electromagnetic Fields by : Dianjing Liu
Download or read book The Application of Machine Learning for Designing and Controlling Electromagnetic Fields written by Dianjing Liu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is the study of computer algorithms that improve automatically through experience. In contrary to rule-based artificial intelligence which produces pre-defined outcomes based on manually coded rules, machine learning algorithms aimed at building models and making decisions based on the sampled data, and without explicitly programmed to do so. Recently, deep neural network-based machine learning algorithms achieved great success in many applications including image recognition, speech recognition, natural language understanding, etc, while their potentials in other domains are to be explored. In this thesis, we explore the application of deep-learning-based algorithms for designing and controlling electromagnetic fields. Firstly, we design the nano-scale structure of the optical medium to change its interaction with the electromagnetic field. This process is called inverse design and is a common problem in nanophotonics. Since an optical property can be achieved by more than one structure, the same design request can have multiple candidate solutions. This issue is called non-uniqueness and it fundamentally makes the direct training of an inverse design neural network hard to converge. We propose a deep-learning-based approach to overcome the non-uniqueness issue and train a neural network as an inverse design toolbox. Once the model is trained, it generates a design for input requests in a fraction of a second without needing any iterative optimization. Another application in photonics is the spontaneous development of the imaging system and the neural network. Typically in deep learning algorithms, the inputs to the neural networks are handcrafted representations of the data. For example, a fully connected neural network requires manually created feature vectors as the inputs. Compared with the fully connected network, the convolutional neural network can process the raw pixel values (i.e., the digital image) and therefore requires less feature engineering. However, these digital images are collected by sensory functions (usually a camera) which are also designed by human intelligence. Here we set up a reinforcement learning agent with the ability to develop a sensory function by itself. We show that although the agent does not have a functional visual sensor to observe the environment at the beginning, it is able to automatically develop parabolic imaging optics and detect a clear visual representation of the environment. Finally, we apply machine learning algorithms for the controlling of electromagnetic fields. A reinforcement learning agent controls the electromagnets to manipulate the spatial distribution of the magnetic field. We demonstrate that this field manipulation is able to levitate and control a magnetic object. The reinforcement learning agent develops the control strategy from experiences and under the guidance of the rewards. The trained agent shows good control skills, and is faster, and has less overshoot compared with the traditional PID controller.
Book Synopsis Investigating the Application of Deep Learning for Electromagnetic Simulation Prediction by : Steven Ryan Price
Download or read book Investigating the Application of Deep Learning for Electromagnetic Simulation Prediction written by Steven Ryan Price and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applications of Neural Networks in Electromagnetics by : Christos Christodoulou
Download or read book Applications of Neural Networks in Electromagnetics written by Christos Christodoulou and published by Artech House Publishers. This book was released on 2001 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The high-speed capabilities and learning abilities of neural networks can be applied to quickly solving numerous complex optimization problems in electromagnetics, and this book shows you how. Even if you have no background in neural networks, this book helps you understand the basics of each main network architecture in use today, including its strengths and limitations. Moreover, it gives you the knowledge you need to identify situations when the use of neural networks is the best problem-solving option.
Book Synopsis Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning by : Qiang Ren
Download or read book Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning written by Qiang Ren and published by Springer Nature. This book was released on 2021-10-20 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
Book Synopsis Nanophotonics and Machine Learning by : Kan Yao
Download or read book Nanophotonics and Machine Learning written by Kan Yao and published by Springer Nature. This book was released on 2023-03-27 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, the first of its kind, bridges the gap between the increasingly interlinked fields of nanophotonics and artificial intelligence (AI). While artificial intelligence techniques, machine learning in particular, have revolutionized many different areas of scientific research, nanophotonics holds a special position as it simultaneously benefits from AI-assisted device design whilst providing novel computing platforms for AI. This book is aimed at both researchers in nanophotonics who want to utilize AI techniques and researchers in the computing community in search of new photonics-based hardware. The book guides the reader through the general concepts and specific topics of relevance from both nanophotonics and AI, including optical antennas, metamaterials, metasurfaces, and other photonic devices on the one hand, and different machine learning paradigms and deep learning algorithms on the other. It goes on to comprehensively survey inverse techniques for device design, AI-enabled applications in nanophotonics, and nanophotonic platforms for AI. This book will be essential reading for graduate students, academic researchers, and industry professionals from either side of this fast-developing, interdisciplinary field.
Book Synopsis Deep Learning Applications with Practical Measured Results in Electronics Industries by : Mong-Fong Horng
Download or read book Deep Learning Applications with Practical Measured Results in Electronics Industries written by Mong-Fong Horng and published by MDPI. This book was released on 2020-05-22 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.
Book Synopsis Deep Learning for Power System Applications by : Fangxing Li
Download or read book Deep Learning for Power System Applications written by Fangxing Li and published by Springer Nature. This book was released on 2023-12-12 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies.
Book Synopsis Machine Learning Paradigms by : George A. Tsihrintzis
Download or read book Machine Learning Paradigms written by George A. Tsihrintzis and published by Springer Nature. This book was released on 2020-07-23 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
Book Synopsis Geophysical Inversion by : J. Bee Bednar
Download or read book Geophysical Inversion written by J. Bee Bednar and published by SIAM. This book was released on 1992-01-01 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of papers on geophysical inversion contains research and survey articles on where the field has been and where it's going, and what is practical and what is not. Topics covered include seismic tomography, migration and inverse scattering.
Book Synopsis Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems by : Yinpeng Wang
Download or read book Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems written by Yinpeng Wang and published by CRC Press. This book was released on 2023-07-06 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems. Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced. As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
Book Synopsis Handbook of Deep Learning Applications by : Valentina Emilia Balas
Download or read book Handbook of Deep Learning Applications written by Valentina Emilia Balas and published by Springer. This book was released on 2019-02-25 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
Book Synopsis Deep Neural Network Design for Radar Applications by : Sevgi Zubeyde Gurbuz
Download or read book Deep Neural Network Design for Radar Applications written by Sevgi Zubeyde Gurbuz and published by SciTech Publishing. This book was released on 2020-12-31 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of.
Book Synopsis Deep Learning Applications: In Computer Vision, Signals And Networks by : Qi Xuan
Download or read book Deep Learning Applications: In Computer Vision, Signals And Networks written by Qi Xuan and published by World Scientific. This book was released on 2023-03-21 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks.The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.
Book Synopsis Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning by : Sawyer D. Campbell
Download or read book Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning written by Sawyer D. Campbell and published by John Wiley & Sons. This book was released on 2023-08-03 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.
