Deterministic Stochastic And Deep Learning Methods For Computational Electromagnetics
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Deterministic Stochastic And Deep Learning Methods For Computational Electromagnetics
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Author : Wei Cai
language : en
Publisher: Springer Nature
Release Date : 2025-03-02
Deterministic Stochastic And Deep Learning Methods For Computational Electromagnetics written by Wei Cai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-02 with Mathematics categories.
This book provides a well-balanced and comprehensive picture based on clear physics, solid mathematical formulation, and state-of-the-art useful numerical methods in deterministic, stochastic, deep neural network machine learning approaches for computer simulations of electromagnetic and transport processes in biology, microwave and optical wave devices, and nano-electronics. Computational research has become strongly influenced by interactions from many different areas including biology, physics, chemistry, engineering, etc. A multifaceted approach addressing the interconnection among mathematical algorithms and physical foundation and application is much needed to prepare graduate students and researchers in applied mathematics and sciences and engineering for innovative advanced computational research in many applications areas, such as biomolecular solvation in solvents, radar wave scattering, the interaction of lights with plasmonic materials, plasma physics, quantum dots, electronic structure, current flows in nano-electronics, and microchip designs, etc.
Deterministic And Stochastic Modeling In Computational Electromagnetics
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Author : Dragan Poljak
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-07
Deterministic And Stochastic Modeling In Computational Electromagnetics written by Dragan Poljak and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-07 with Science categories.
Deterministic and Stochastic Modeling in Computational Electromagnetics Help protect your network with this important reference work on cyber security Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges. Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic–stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models. Readers will also find: A range of specific examples demonstrating the efficiency of deterministic–stochastic modeling Computational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and more Introduction to fundamental principles in field theory to ground the discussion of computational modeling Deterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.
Theory And Computation Of Electromagnetic Fields In Layered Media
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Author : Vladimir Okhmatovski
language : en
Publisher: John Wiley & Sons
Release Date : 2024-04-23
Theory And Computation Of Electromagnetic Fields In Layered Media written by Vladimir Okhmatovski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-23 with Science categories.
Explore the algorithms and numerical methods used to compute electromagnetic fields in multi-layered media In Theory and Computation of Electromagnetic Fields in Layered Media, two distinguished electrical engineering researchers deliver a detailed and up-to-date overview of the theory and numerical methods used to determine electromagnetic fields in layered media. The book begins with an introduction to Maxwell’s equations, the fundamentals of electromagnetic theory, and concepts and definitions relating to Green’s function. It then moves on to solve canonical problems in vertical and horizontal dipole radiation, describe Method of Moments schemes, discuss integral equations governing electromagnetic fields, and explains the Michalski-Zheng theory of mixed-potential Green’s function representation in multi-layered media. Chapters on the evaluation of Sommerfeld integrals, procedures for far field evaluation, and the theory and application of hierarchical matrices are also included, along with: A thorough introduction to free-space Green’s functions, including the delta-function model for point charge and dipole current Comprehensive explorations of the traditional form of layered medium Green’s function in three dimensions Practical discussions of electro-quasi-static and magneto-quasi-static fields in layered media, including electrostatic fields in two and three dimensions In-depth examinations of the rational function fitting method, including direct spectra fitting with VECTFIT algorithms Perfect for scholars and students of electromagnetic analysis in layered media, Theory and Computation of Electromagnetic Fields in Layered Media will also earn a place in the libraries of CAD industry engineers and software developers working in the area of computational electromagnetics.
Advances In Electromagnetics Empowered By Artificial Intelligence And Deep Learning
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Author : Sawyer D. Campbell
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-03
Advances In Electromagnetics Empowered By Artificial Intelligence And Deep Learning written by Sawyer D. Campbell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-03 with Technology & Engineering categories.
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.
Topology Optimization And Ai Based Design Of Power Electronic And Electrical Devices
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Author : Hajime Igarashi
language : en
Publisher: Elsevier
Release Date : 2024-01-15
Topology Optimization And Ai Based Design Of Power Electronic And Electrical Devices written by Hajime Igarashi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-15 with Technology & Engineering categories.
Topology Optimization and AI-based Design of Power Electronic and Electrical Devices: Principles and Methods provides an essential foundation in the emergent design methodology as it moves towards commercial development in such electrical devices as traction motors for electric motors, transformers, inductors, reactors and power electronics circuits. Opening with an introduction to electromagnetism and computational electromagnetics for optimal design, the work outlines principles and foundations in finite element methods and illustrates numerical techniques useful for finite element analysis. It summarizes the foundations of deterministic and stochastic optimization methods, including genetic algorithm, particle swarm optimization and simulated annealing, alongside representative algorithms. The work goes on to discuss parameter optimization and topology optimization of electrical devices alongside current implementations including magnetic shields, 2D and 3D models of electric motors, and wireless power transfer devices. The work concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, deep neural networks, and automatic design methods using Monte-Carlo tree searches for electrical devices and circuits. - Assists researchers and design engineers in applying emergent topology design optimization to power electronics and electrical device design, supported by step-by-step methods, heuristic derivation, and pseudocodes - Proposes unique formulations of AI-based design for electrical devices using Monte Carlo tree search and other machine learning methods - Is richly accompanied by detailed numerical examples and repletes with computational support materials in algorithms and explanatory formulae - Includes access to pedagogical videos on topics including the evolutionary process of topology optimization, the distribution of genetic algorithms, and CMA-ES
American Book Publishing Record
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Author : R.R. Bowker Company
language : en
Publisher: R. R. Bowker
Release Date : 1978
American Book Publishing Record written by R.R. Bowker Company and has been published by R. R. Bowker this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Reference categories.
Stochastic Techniques For Computational Electromagnetics And Signal Integrity Design Optimization
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Author :
language : en
Publisher:
Release Date : 2014
Stochastic Techniques For Computational Electromagnetics And Signal Integrity Design Optimization written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Machine Learning
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Author : Sergios Theodoridis
language : en
Publisher: Academic Press
Release Date : 2015-04-02
Machine Learning written by Sergios Theodoridis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-02 with Technology & Engineering categories.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. - All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. - The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. - Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. - MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
Stochastic Modeling In Computational Electromagnetics
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Author :
language : en
Publisher:
Release Date : 2013
Stochastic Modeling In Computational Electromagnetics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Numerical Methods And Deep Learning For Stochastic Control Problems And Partial Differential Equations
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Author : Come Huré
language : en
Publisher:
Release Date : 2019
Numerical Methods And Deep Learning For Stochastic Control Problems And Partial Differential Equations written by Come Huré and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
The present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial differential equations (PDEs), quasi-variational inequalities (QVIs), backward stochastic differential equations (BSDEs) and reflected backward stochastic differential equations (RBSDEs). The thesis is divided into three parts.The first part focuses on methods based on quantization, local regression and global regression to solve MDPs. Firstly, we present a new algorithm, named Qknn, and study its consistency. A time-continuous control problem of market-making is then presented, which is theoretically solved by reducing the problem to a MDP, and whose optimal control is accurately approximated by Qknn. Then, a method based on Markovian embedding is presented to reduce McKean-Vlasov control prob- lem with partial information to standard MDP. This method is applied to three different McKean- Vlasov control problems with partial information. The method and high accuracy of Qknn is validated by comparing the performance of the latter with some finite difference-based algorithms and some global regression-based algorithm such as regress-now and regress-later.In the second part of the thesis, we propose new algorithms to solve MDPs in high-dimension. Neural networks, combined with gradient-descent methods, have been empirically proved to be the best at learning complex functions in high-dimension, thus, leading us to base our new algorithms on them. We derived the theoretical rates of convergence of the proposed new algorithms, and tested them on several relevant applications.In the third part of the thesis, we propose a numerical scheme for PDEs, QVIs, BSDEs, and RBSDEs. We analyze the performance of our new algorithms, and compare them to other ones available in the literature (including the recent one proposed in [EHJ17]) on several tests, which illustrates the efficiency of our methods to estimate complex solutions in high-dimension.Keywords: Deep learning, neural networks, Stochastic control, Markov Decision Process, non- linear PDEs, QVIs, optimal stopping problem BSDEs, RBSDEs, McKean-Vlasov control, perfor- mance iteration, value iteration, hybrid iteration, global regression, local regression, regress-later, quantization, limit order book, pure-jump controlled process, algorithmic-trading, market-making, high-dimension.