Considers the formulation of stochastic equations relevant to applications
Focuses on methods for solving realistic stochastic problems
Specifies the stochastic equations under considerations by mathematical arguments, rather than inferring the definition of these equations from applications
Provides practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions
Mircea Grigoriu is a professor at Cornell University whose research has focused primarily on applications of probability theory to applied sciences and engineering. His contributions to probabilistic models for actions and physical properties, random vibration, stochastic mechanics, system reliability, and Monte Carlo simulation are reported in over 200 technical papers, three books, and this new book on Stochastic Systems. His work has been recognized by numerous prizes, for example, the 2002 Alfred Freudenthal Medal, the election to the Romanian Academy of Technical Sciences in 2004, and the 2005 Norman Medal.
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.
Probability Essentials.- Random Functions.- Probabilistic Models.- Stochastic Integrals and Itô's Formula.- Properties of Solutions of Stochastic Equations.- Stochastic Equations with Small Uncertainty.- Stochastic Algebraic Equations.- Stochastic Differential Equations with Deterministic Coefficients.- Stochastic Differential Equations with Random Coefficients.
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)
This book offers accurate, efficient methods for solving stochastic equations, with a focus on formulating equations that apply to problems in engineering and applied sciences. Numerous examples illustrate essential theoretical concepts, models and methods.
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.
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)
Über den Autor
Mircea Grigoriu is a professor at Cornell University whose research has focused primarily on applications of probability theory to applied sciences and engineering. His contributions to probabilistic models for actions and physical properties, random vibration, stochastic mechanics, system reliability, and Monte Carlo simulation are reported in over 200 technical papers, three books, and this new book on Stochastic Systems. His work has been recognized by numerous prizes, for example, the 2002 Alfred Freudenthal Medal, the election to the Romanian Academy of Technical Sciences in 2004, and the 2005 Norman Medal.
Inhaltsverzeichnis
Probability Essentials.- Random Functions.- Probabilistic Models.- Stochastic Integrals and Itô's Formula.- Properties of Solutions of Stochastic Equations.- Stochastic Equations with Small Uncertainty.- Stochastic Algebraic Equations.- Stochastic Differential Equations with Deterministic Coefficients.- Stochastic Differential Equations with Random Coefficients.
Klappentext
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:n A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysisn n Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciencesn n Practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutionsn nStochastic 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.
Considers the formulation of stochastic equations relevant to applications
Focuses on methods for solving realistic stochastic problems
Specifies the stochastic equations under considerations by mathematical arguments, rather than inferring the definition of these equations from applications
Provides practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions
Includes supplementary material: sn.pub/extras
