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Stochastic equations in infinite dimensions
Authors: ---
ISBN: 0521385296 9780521385299 Year: 1992 Volume: v. 45[i.e. 44] Publisher: Cambridge Cambridge University Press

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Abstract

The aim of this book is to give a systematic and self-contained presentation of the basic results on stochastic evolution equations in infinite dimensional, typically Hilbert and Banach, spaces. These are a generalization of stochastic differential equations as introduced by It and Gikhman that occur, for instance, when describing random phenomena that crop up in science and engineering, as well as in the study of differential equations. The book is divided into three parts. In the first the authors give a self-contained exposition of the basic properties of probability measures on separable Banach and Hilbert spaces, as required later; they assume a reasonable background in probability theory and finite dimensional stochastic processes. The second part is devoted to the existence and uniqueness of solutions of a general stochastic evolution equation, and the third concerns the qualitative properties of those solutions. Appendices gather together background results from analysis that are otherwise hard to find under one roof.

Stochastic partial differential equations : a modeling, white noise functional approach
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ISBN: 0817639284 3764339284 9783764339289 9780817639280 Year: 1996 Publisher: Boston : Birkhauser Boston,

Stochastic partial differential equations /.
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ISBN: 9781584884439 1584884436 0367453126 0429101112 0429147031 1466579552 1466579579 9780429147036 Year: 2007 Publisher: Boca Raton Taylor & Francis

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Filling the void of an introductory text in the field, this book highlights several computational and analytical techniques involved in stochastic PDEs. It includes many challenging problems in stochastic analysis and treats stochastic PDEs in a practical way. The author first brings the subject back to its root in classical concrete problems. He then discusses a unified theory of stochastic evolution equations and describes a few applied problems, including the random vibration of a nonlinear elastic beam and invariant measures for stochastic Navier-Stokes equations. The book concludes by pointing out the connection of stochastic PDEs to infinite-dimensional stochastic analysis.

Stochastic partial differential equations with Lévy noise : an evolution equation approach
Authors: ---
ISBN: 9780521879897 0521879892 9780511721373 9781107089754 1107089751 9781107096059 1107096057 1139883437 9781139883436 1107101654 9781107101654 1107104084 9781107104082 0511721374 Year: 2007 Volume: v. 113 Publisher: Cambridge : Cambridge University Press,

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Abstract

Recent years have seen an explosion of interest in stochastic partial differential equations where the driving noise is discontinuous. In this comprehensive monograph, two leading experts detail the evolution equation approach to their solution. Most of the results appeared here for the first time in book form. The authors start with a detailed analysis of Lévy processes in infinite dimensions and their reproducing kernel Hilbert spaces; cylindrical Lévy processes are constructed in terms of Poisson random measures; stochastic integrals are introduced. Stochastic parabolic and hyperbolic equations on domains of arbitrary dimensions are studied, and applications to statistical and fluid mechanics and to finance are also investigated. Ideal for researchers and graduate students in stochastic processes and partial differential equations, this self-contained text will also interest those working on stochastic modeling in finance, statistical physics and environmental science.


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An introduction to computational stochastic PDEs
Authors: --- ---
ISBN: 9780521899901 0521899907 9780521728522 0521728525 9781139017329 Year: 2014 Publisher: New York, NY, USA Cambridge University Press

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"This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method. Later chapters apply the theory of random fields to the numerical solution of elliptic PDEs with correlated random data, discuss the Monte Carlo method, and introduce stochastic Galerkin finite-element methods. Finally, stochastic parabolic PDEs are developed. Assuming little previous exposure to probability and statistics, theory is developed in tandem with state-of the art computational methods through worked examples, exercises, theorems and proofs. The set of MATLAB codes included (and downloadable) allows readers to perform computations themselves and solve the test problems discussed. Practical examples are drawn from finance, mathematical biology, neuroscience, fluid flow modeling and materials science"--

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