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Numerical Methods For Stochastic Computations

Author: Dongbin Xiu
Publisher: Princeton University Press
ISBN: 9781400835348
Size: 50.83 MB
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[email protected] first graduate-level textbook to focus on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). These fast, efficient, and accurate methods are an extension of the classical spectral methods of high-dimensional random spaces. Designed to simulate complex systems subject to random inputs, these methods are widely used in many areas of computer science and engineering. The book introduces polynomial approximation theory and probability theory; describes the basic theory of gPC methods through numerical examples and rigorous development; details the procedure for converting stochastic equations into deterministic ones; using both the Galerkin and collocation approaches; and discusses the distinct differences and challenges arising from high-dimensional problems. The last section is devoted to the application of gPC methods to critical areas such as inverse problems and data assimilation. Ideal for use by graduate students and researchers both in the classroom and for self-study, Numerical Methods for Stochastic Computations provides the required tools for in-depth research related to stochastic computations. The first graduate-level textbook to focus on the fundamentals of numerical methods for stochastic computations Ideal introduction for graduate courses or self-study Fast, efficient, and accurate numerical methods Polynomial approximation theory and probability theory included Basic gPC methods illustrated through examples

Numerical Methods For Stochastic Partial Differential Equations With White Noise

Author: Zhongqiang Zhang
Publisher: Springer
ISBN: 3319575112
Size: 13.95 MB
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This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation. The book begins with some motivational and background material in the introductory chapters and is divided into three parts. Part I covers numerical stochastic ordinary differential equations. Here the authors start with numerical methods for SDEs with delay using the Wong-Zakai approximation and finite difference in time. Part II covers temporal white noise. Here the authors consider SPDEs as PDEs driven by white noise, where discretization of white noise (Brownian motion) leads to PDEs with smooth noise, which can then be treated by numerical methods for PDEs. In this part, recursive algorithms based on Wiener chaos expansion and stochastic collocation methods are presented for linear stochastic advection-diffusion-reaction equations. In addition, stochastic Euler equations are exploited as an application of stochastic collocation methods, where a numerical comparison with other integration methods in random space is made. Part III covers spatial white noise. Here the authors discuss numerical methods for nonlinear elliptic equations as well as other equations with additive noise. Numerical methods for SPDEs with multiplicative noise are also discussed using the Wiener chaos expansion method. In addition, some SPDEs driven by non-Gaussian white noise are discussed and some model reduction methods (based on Wick-Malliavin calculus) are presented for generalized polynomial chaos expansion methods. Powerful techniques are provided for solving stochastic partial differential equations. This book can be considered as self-contained. Necessary background knowledge is presented in the appendices. Basic knowledge of probability theory and stochastic calculus is presented in Appendix A. In Appendix B some semi-analytical methods for SPDEs are presented. In Appendix C an introduction to Gauss quadrature is provided. In Appendix D, all the conclusions which are needed for proofs are presented, and in Appendix E a method to compute the convergence rate empirically is included. In addition, the authors provide a thorough review of the topics, both theoretical and computational exercises in the book with practical discussion of the effectiveness of the methods. Supporting Matlab files are made available to help illustrate some of the concepts further. Bibliographic notes are included at the end of each chapter. This book serves as a reference for graduate students and researchers in the mathematical sciences who would like to understand state-of-the-art numerical methods for stochastic partial differential equations with white noise.

Data Assimilation For Atmospheric Oceanic And Hydrologic Applications

Author: Seon Ki Park
Publisher: Springer
ISBN: 3319434152
Size: 73.64 MB
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This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.

Uncertainty Quantification In Computational Fluid Dynamics And Aircraft Engines

Author: Francesco Montomoli
Publisher: Springer
ISBN: 3319929437
Size: 68.67 MB
Format: PDF, Kindle
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This book introduces design techniques developed to increase the safety of aircraft engines, and demonstrates how the application of stochastic methods can overcome problems in the accurate prediction of engine lift caused by manufacturing error. This in turn addresses the issue of achieving required safety margins when hampered by limits in current design and manufacturing methods. The authors show that avoiding the potential catastrophe generated by the failure of an aircraft engine relies on the prediction of the correct behaviour of microscopic imperfections. This book shows how to quantify the possibility of such failure, and that it is possible to design components that are inherently less risky and more reliable. This new, updated and significantly expanded edition gives an introduction to engine reliability and safety to contextualise this important issue, evaluates newly-proposed methods for uncertainty quantification as applied to jet engines. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines will be of use to gas turbine manufacturers and designers as well as CFD practitioners, specialists and researchers. Graduate and final year undergraduate students in aerospace or mathematical engineering may also find it of interest.

Uncertainty Management For Robust Industrial Design In Aeronautics

Author: Charles Hirsch
Publisher: Springer
ISBN: 331977767X
Size: 60.45 MB
Format: PDF
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This book covers cutting-edge findings related to uncertainty quantification and optimization under uncertainties (i.e. robust and reliable optimization), with a special emphasis on aeronautics and turbomachinery, although not limited to these fields. It describes new methods for uncertainty quantification, such as non-intrusive polynomial chaos, collocation methods, perturbation methods, as well as adjoint based and multi-level Monte Carlo methods. It includes methods for characterization of most influential uncertainties, as well as formulations for robust and reliable design optimization. A distinctive element of the book is the unique collection of test cases with prescribed uncertainties, which are representative of the current engineering practice of the industrial consortium partners involved in UMRIDA, a level 1 collaborative project within the European Commission's Seventh Framework Programme (FP7). All developed methods are benchmarked against these industrial challenges. Moreover, the book includes a section dedicated to Best Practice Guidelines for uncertainty quantification and robust design optimization, summarizing the findings obtained by the consortium members within the UMRIDA project. All in all, the book offers a authoritative guide to cutting-edge methodologies for uncertainty management in engineering design, covers a wide range of applications and discusses new ideas for future research and interdisciplinary collaborations.

Relativistische Quantenmechanik

Author: Armin Wachter
Publisher: Springer-Verlag
ISBN: 3540274847
Size: 78.70 MB
Format: PDF
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- Welche Probleme tauchen in relativistischen Erweiterungen der Schrödinger-Theorie auf, insbesondere wenn man an der gewohnten Ein-Teilchen-Wahrscheinlichkeitsinterpretation festhält? - Inwieweit können diese Schwierigkeiten überwunden werden? - Worin besteht die physikalische Notwendigkeit von Quantenfeldtheorien? Viele Bücher geben auf solch fundamentale Verständnisfragen nur unzureichend Antwort, indem sie das relativistisch-quantenmechanische Ein-Teilchenkonzept zugunsten einer möglichst frühen Einführung der Feldquantisierung relativ schnell abhandeln oder ganz weglassen. Im Gegensatz dazu betont das vorliegende Lehrbuch gerade diesen Ein-Teilchenaspekt (relativistische Quantenmechanik ‚im engeren Sinne’), diskutiert die damit einhergehenden Probleme und motiviert somit auf physikalisch verständliche Weise die Notwendigkeit quantisierter Felder. Die ersten beiden Kapitel beschäftigen sich mit der ausführlichen Darlegung und Gegenüberstellung der Klein-Gordon- und Dirac-Theorie - immer mit Blick auf die nichtrelativistische Theorie. Im dritten Kapitel werden relativistische Streuprozesse behandelt und die Feynman-Regeln aus Propagatorverfahren heraus entwickelt. Dabei wird auch hier deutlich, warum man letztlich um eine quantenfeldtheoretische Begründung nicht herumkommt. Dieses Lehrbuch wendet sich an alle Studierenden der Physik, die an einer übersichtlich geordneten Darstellung der relativistischen Quantenmechanik ‚im engeren Sinne’ und deren Abgrenzung zu Quantenfeldtheorien interessiert sind.

Wissenschaftliches Rechnen Mit Matlab

Author: Alfio Quarteroni
Publisher: Springer-Verlag
ISBN: 3540293078
Size: 20.34 MB
Format: PDF
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Aus den Rezensionen der englischen Auflage: Dieses Lehrbuch ist eine Einführung in das Wissenschaftliche Rechnen und diskutiert Algorithmen und deren mathematischen Hintergrund. Angesprochen werden im Detail nichtlineare Gleichungen, Approximationsverfahren, numerische Integration und Differentiation, numerische Lineare Algebra, gewöhnliche Differentialgleichungen und Randwertprobleme. Zu den einzelnen Themen werden viele Beispiele und Übungsaufgaben sowie deren Lösung präsentiert, die durchweg in MATLAB formuliert sind. Der Leser findet daher nicht nur die graue Theorie sondern auch deren Umsetzung in numerischen, in MATLAB formulierten Code. MATLAB select 2003, Issue 2, p. 50. [Die Autoren] haben ein ausgezeichnetes Werk vorgelegt, das MATLAB vorstellt und eine sehr nützliche Sammlung von MATLAB Funktionen für die Lösung fortgeschrittener mathematischer und naturwissenschaftlicher Probleme bietet. [...] Die Präsentation des Stoffs ist durchgängig gut und leicht verständlich und beinhaltet Lösungen für die Übungen am Ende jedes Kapitels. Als exzellenter Neuzugang für Universitätsbibliotheken- und Buchhandlungen wird dieses Buch sowohl beim Selbststudium als auch als Ergänzung zu anderen MATLAB-basierten Büchern von großem Nutzen sein. Alles in allem: Sehr empfehlenswert. Für Studenten im Erstsemester wie für Experten gleichermassen. S.T. Karris, University of California, Berkeley, Choice 2003.

Numerische Methoden

Author: John Douglas Faires
Publisher: Springer Verlag
ISBN: 9783827405968
Size: 55.81 MB
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Numerische Methoden a " NAherungsverfahren also a " sind im allgemeinen Bestandteil von Vorlesungen zur numerischen Analysis. Der Vorteil: Wissenschaftliche GrA1/4ndlichkeit, AusfA1/4hrlichkeit der BeweisfA1/4hrung. Der Nachteil: Mangel an praktischem Nutzen a " u.a. fA1/4r den (angehenden) Natur- und Ingenieurwissenschaftler. Faires und Burden haben daher Ballast abgeworfen: Die Betonung ihres Werkes "Numerische Methoden" liegt in der Anwendung von NAherungsverfahren a " und zwar auf solche Probleme, die fA1/4r Natur- und Ingenieurwissenschaftler charakteristisch sind. Alle Verfahren werden unter dem Aspekt der Implementierung beschrieben und eine vollstAndige mathematische BegrA1/4ndung nur dann diskutiert, falls sie beitrAgt, das Verfahren zu verstehen. Mit der beigefA1/4gten Software a " in FORTRAN und Pascal a " lassen sich die meisten der gestellten Probleme lAsen. "Numerische Methoden" ist so mit Lehrbuch und Nachschlagewerk zugleich.

Vorlesungen Ber Variationsrechnung

Author: Oskar Bolza
Publisher: American Mathematical Soc.
ISBN: 9780828401609
Size: 67.46 MB
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A standard text and reference work, by one of the major contributors to that theory. The text is in German and includes 117 figures.