Springer Stochastic Systems: Uncertainty Quantification and Propagation (Springer Series in Reliability Engineering)

Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents:          A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis             Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciences             Practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions   Stochastic Systems provides key information for researchers, graduate students, and engineers who are interested in the formulation and solution of stochastic problems encountered in a broad range of disciplines. Numerous examples are used to clarify and illustrate theoretical concepts and methods for solving stochastic equations. The extensive bibliography and index at the end of the book constitute an ideal resource for both theoreticians and practitioners. Review From the reviews: “Monograph provides a broad overview over the power of stochastic systems on a high mathematical level. It is aimed at interested readers from various fields of science and practitioners … . provides the mathematical understanding to a broad spectrum of systems subject to randomness and a wast repertoire of techniques to tackle these phenomena. … great source for practitioners and scientists of various fields and will equip the reader with the knowledge to properly formulate his models and to derive the understanding of their behavior.” (Jan Gairing, Zentralblatt MATH, Vol. 1247, 2012) “The book deals with theoretical and computational aspects of stochastic equations. … The book is self-contained and can be used for teaching graduate courses. … Each chapter has illustrative examples and end of chapter problems which are useful for preparing a graduate course or for readers who will use this book for self-education.” (Mikhail V. Tretyakov, Mathematical Reviews, January, 2013) From the Back Cover Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: ·         A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis   ·          Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied scien