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Here is a book that provides the classical foundations of invariant imbedding, a concept that provided the first indication of the connection between transport theory and the Riccati Equation. The reprinting of this classic volume was prompted by a revival of interest in the subject area because of its uses for inverse problems. The major part of the book consists of applications of the invariant imbedding method to specific areas that are of interest to engineers, physicists, applied mathematicians, and numerical analysts. A large set of problems can be found at the end of each chapter. Numerous problems on apparently disparate matters such as Riccati equations, continued fractions, functional equations, and Laplace transforms are included. The exercises present the reader with "real-life" situations. The material is accessible to a general audience, however, the authors do not hesitate to state, and even to prove, a rigorous theorem when one is available. To keep the original flavor of the book, very few changes were made to the manuscript; typographical errors were corrected and slight changes in word order were made to reduce ambiguities.
Imbedding is a powerful and versatile tool for problem solving. Rather than treat a question in isolation, we view it as a member of a family of related problems. Each member then becomes a stepping stone in a path to a simultaneous solution of the entire set of problems. As might be expected, there are many ways of accomplishing this imbedding. Time and space variables have been widely employed in the past, while modern approaches combine these structural features with others less immediate. Why should one search for alternate imbeddings when elegant classical formalisms already exist? There are many reasons. To begin with, different imbeddings are useful for different purposes. Some are well suited to the derivation of existence and uniqueness theorems, some to the derivation of conservation relations, some to perturbation techniques and sensitivity analysis, some to computa tional studies. The digital computer is designed for initial value problems; the analog computer for boundary-value problems. It is essential then to be flexible and possess the ability to use one device or the other, or both. In economics, engineering, biology and physics, some pro cesses lend themselves more easily to one type of imbedding rather than another. Thus, for example, stochastic decision processes are well adapted to dynamic programming. In any case, to go hunting in the wilds of the scientific world armed with only one arrow in one's quiver is quite foolhardy.
Invariant Imbedding T-matrix Method for Light Scattering by Nonspherical and Inhomogeneous Particles propels atmospheric research forward as a resource and a tool for understanding the T-Matrix method in relation to light scattering. The text explores concepts ranging from electromagnetic waves and scattering dyads to the fundamentals of the T-Matrix method. Providing recently developed material, this text is sufficient to aid the light scattering science community with current and leading information. Enriched with detailed research from top field experts, Invariant Imbedding T-matrix Method for Light Scattering by Nonspherical and Inhomogeneous Particles offers a meaningful and essential presentation of methods and applications, with a focus on the light scattering of small and intermediate particles that supports and builds upon the latest studies. Thus, it is a valuable resource for atmospheric researchers and other earth and environmental scientists to expand their knowledge and understanding of available tools. - Systematically introduces innovative methods with powerful numerical capabilities - Thoroughly presents the rudimentary principles of light scattering and the T-matrix method - Offers a condensed and well-ordered arrangement of text, figures and formulas that are serviceable for both students and researchers
Methods of Nonlinear Analysis
A modern treatment of both direct and inverse problems applicable to the remote sensing of earth from space or from the air. Starting from a physical description of the process, the authors develop innovative mathematical models, fundamental mathematics for the analysis of these models, and methods for obtaining computational solutions. They also include the results of recent research using this approach, such as invariant imbedding techniques, associative memory artificial neural networks, and the automatic evaluation of derivatives. With its coverage of uniform parallel illumination, internal sources, and incident spotlight beams, this book is indispensable for researchers working to reduce the atmospheric distortion of remotely sensed terrestrial images.