Computational electromagnetics is an active research area concerned with the development and implementation of numerical methods and techniques for rigorous solutions to physical problems across the entire spectrum of electromagnetic waves - from radio frequencies to gamma rays. Numerical methods and techniques developed and implemented in this area are now used every day to solve complex problems in diverse application areas, including but not limited to antennas, telecommunications, biomedical imaging, sensing, energy harvesting, nanotechnology, and optics. The purpose of this book is to provide a broad overview of the recent efforts in computational electromagnetics to develop and implement more robust, stable, accurate, and efficient algorithms. After an extensive overview of the main trends in computational electromagnetics, individual chapters written by international experts explore the state-of-the-art in frequency-domain surface integration; frequency-domain volume integral equations; time-domain integral equations; time-domain methods for plasmonic media; finite element methods; geometric modelling and discretization for integral equations; hierarchical vector basis functions; analysis of electromagnetic fields in multilayered media; acceleration and parallelization techniques; periodic problems and determining related eigenvalues; algebraic preconditioning; high-frequency techniques and hybridizations; and uncertainty quantification for large-scale electromagnetic analysis.

This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.

This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.