The purpose of this book is to provide an up-to-date introduction to the time-domain finite element methods for Maxwell’s equations involving metamaterials. Since the first successful construction of a metamaterial with both negative permittivity and permeability in 2000, the study of metamaterials has attracted significant attention from researchers across many disciplines. Thanks to enormous efforts on the part of engineers and physicists, metamaterials present great potential applications in antenna and radar design, sub-wavelength imaging, and invisibility cloak design. Hence the efficient simulation of electromagnetic phenomena in metamaterials has become a very important issue and is the subject of this book, in which various metamaterial modeling equations are introduced and justified mathematically. The development and practical implementation of edge finite element methods for metamaterial Maxwell’s equations are the main focus of the book. The book finishes with some interesting simulations such as backward wave propagation and time-domain cloaking with metamaterials.

Finite Element Methods For Maxwell's Equations is the first book to present the use of finite elements to analyse Maxwell's equations. This book is part of the Numerical Analysis and Scientific Computation Series.

This book provides a brief overview of the popular Finite Element Method (FEM) and its hybrid versions for electromagnetics with applications to radar scattering, antennas and arrays, guided structures, microwave components, frequency selective surfaces, periodic media, and RF materials characterizations and related topics. It starts by presenting concepts based on Hilbert and Sobolev spaces as well as Curl and Divergence spaces for generating matrices, useful in all engineering simulation methods. It then proceeds to present applications of the finite element and finite element-boundary integral methods for scattering and radiation. Applications to periodic media, metamaterials and bandgap structures are also included. The hybrid volume integral equation method for high contrast dielectrics and is presented for the first time. Another unique feature of the book is the inclusion of design optimization techniques and their integration within commercial numerical analysis packages for shape and material design. To aid the reader with the method's utility, an entire chapter is devoted to two-dimensional problems. The book can be considered as an update on the latest developments since the publication of our earlier book (Finite Element Method for Electromagnetics, IEEE Press, 1998). The latter is certainly complementary companion to this one.

In this popular text for an Numerical Analysis course, the authors introduce several major methods of solving various partial differential equations (PDEs) including elliptic, parabolic, and hyperbolic equations. It covers traditional techniques including the classic finite difference method, finite element method, and state-of-the-art numercial methods.The text uniquely emphasizes both theoretical numerical analysis and practical implementation of the algorithms in MATLAB. This new edition includes a new chapter, Finite Value Method, the presentation has been tightened, new exercises and applications are included, and the text refers now to the latest release of MATLAB. Key Selling Points: A successful textbook for an undergraduate text on numerical analysis or methods taught in mathematics and computer engineering. This course is taught in every university throughout the world with an engineering department or school. Competitive advantage broader numerical methods (including finite difference, finite element, meshless method, and finite volume method), provides the MATLAB source code for most popular PDEs with detailed explanation about the implementation and theoretical analysis. No other existing textbook in the market offers a good combination of theoretical depth and practical source codes.

Abstract: This dissertation is concerned with the development of numerical techniques for solving Maxwell equations in the time-domain. Two of the main challenges to obtain such solution are, first, how to construct explicit (that is, matrix-free) time-updating formulas without relinquishing the advantage of using irregular unstructured meshes in complex geometries, and second, how to best parallelize the algorithm to solve large-scale problems. The finite element time-domain (FETD) and the finite-difference time-Domain (FDTD) are presently the two most popular methods for solving Maxwell equations in the time-domain. FDTD employs a staggered-grid spatial discretization together with leap-frog style time update scheme to produce a method with many desirable properties such as: conservation of charge and energy, absence of spurious mode, and a simple easy-to-code algorithm. Nevertheless, FDTD (in its conventional form) relies on orthogonal grids, which is a disadvantage when modeling complex geometries. On the other hand, FETD is based upon unstructured grids and hence naturally tailored to handle complex geometries. However, in time-domain simulation (as opposed to frequency-domain simulations), FETD requires a matrix solver at every time step. Since the total number of time steps to produce the overall time-domain solution can be quite large, this requirement demands excessive computational resources. To overcome this problem, we develop a FETD algorithm with "FDTD-like" explicit characteristics. Usually, the system matrices generated after discretizing Maxwell equations in irregular grids are very large and sparse matrices, while their inverses are very large and dense matrices. To construct an explicit algorithm, ideally one would need to somehow obtain and use such inverses. However, these dense matrices are of course not useful in a update scheme because they are not only very costly to compute but also very costly to store for most practical problems. For this reason, we investigate the use of approximate sparse inverses to build update schemes for FETD. We show that the most direct choice, which is to use the approximate inverse of the system matrix itself, is not really an adequate choice because of the nature of the corresponding (continuum) operator, with long-range interactions. We therefore consider instead the use of the approximate inverse of the Hodge (or mass) matrix, which a symmetric positive definite matrix representing a strictly local operator in the continuum limit whose inverse is also local, to compute explicit update schemes. This entails the discretization of Maxwell's equations based on discrete differential forms and the use of a "mixed" set of basis functions for the FETD: Whitney one forms for the electric field intensity and Whitney two forms for the magnetic flux density. This choice of basis functions obeys a discrete version of the de Rham diagram and leads to solutions that are free of spurious modes and numerically stable. We construct a parallel approach to compute the approximate inverse, and provide an error analysis of the resulting solutions versus the density of the approximate inverse and the mesh refinement considered. A higher-order version of the mixed FETD algorithm is also constructed, showing good convergence versus the polynomial order.

This special volume of the conference will be of immense use to the researchers and academicians. In this conference, academicians, technocrats and researchers will get an opportunity to interact with eminent persons in the field of Applied Mathematics and Scientific Computing. The topics to be covered in this International Conference are comprehensive and will be adequate for developing and understanding about new developments and emerging trends in this area. High-Performance Computing (HPC) systems have gone through many changes during the past two decades in their architectural design to satisfy the increasingly large-scale scientific computing demand. Accurate, fast, and scalable performance models and simulation tools are essential for evaluating alternative architecture design decisions for the massive-scale computing systems. This conference recounts some of the influential work in modeling and simulation for HPC systems and applications, identifies some of the major challenges, and outlines future research directions which we believe are critical to the HPC modeling and simulation community.

This volume contains the proceedings of the Eighth International Conference on Scientific Computing and Applications, held April 1-4, 2012, at the University of Nevada, Las Vegas. The papers in this volume cover topics such as finite element methods, multiscale methods, finite difference methods, spectral methods, collocation methods, adaptive methods, parallel computing, linear solvers, applications to fluid flow, nano-optics, biofilms, finance, magnetohydrodynamics flow, electromagnetic waves, the fluid-structure interaction problem, and stochastic PDEs. This book will serve as an excellent reference for graduate students and researchers interested in scientific computing and its applications.

This book addresses recent developments in mathematical analysis and computational methods for solving direct and inverse problems for Maxwell’s equations in periodic structures. The fundamental importance of the fields is clear, since they are related to technology with significant applications in optics and electromagnetics. The book provides both introductory materials and in-depth discussion to the areas in diffractive optics that offer rich and challenging mathematical problems. It is also intended to convey up-to-date results to students and researchers in applied and computational mathematics, and engineering disciplines as well.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on High Performance Computing and Applications, HPCA 2009, held in Shangahi, China, in August 2009. The 71 revised papers presented together with 10 invited presentations were carefully selected from 324 submissions. The papers cover topics such as numerical algorithms and solutions; high performance and grid computing; novel approaches to high performance computing; massive data storage and processsing; and hardware acceleration.

This book constitutes the thoroughly refereed proceedings of the Eleventh International Symposium on Natural Language Processing (SNLP-2016), held in Phranakhon Si Ayutthaya, Thailand on February 10–12, 2016. The SNLP promotes research in natural language processing and related fields, and provides a unique opportunity for researchers, professionals and practitioners to discuss various current and advanced issues of interest in NLP. The 2016 symposium was expanded to include the First Workshop in Intelligent Informatics and Smart Technology. Of the 66 high-quality papers accepted, this book presents twelve from the Symposium on Natural Language Processing track and ten from the Workshop in Intelligent Informatics and Smart Technology track (SSAI: Special Session on Artificial Intelligence).