Spectral Methods for Uncertainty Quantification, Olivier Le Maitre; Omar M Knio

Автор: Olivier Le Maitre; Omar M Knio
Название: Spectral Methods for Uncertainty Quantification
ISBN: 9048135192 ISBN-13(EAN): 9789048135196
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book presents applications of spectral methods to problems of uncertainty propagation and quantification in model-based computations, focusing on the computational and algorithmic features of these methods most useful in dealing with models based on partial differential equations, in particular models arising in simulations of fluid flows.

Автор: Manolis Papadrakakis; George Stefanou
Название: Multiscale Modeling and Uncertainty Quantification of Materials and Structures
ISBN: 3319063308 ISBN-13(EAN): 9783319063300
Издательство: Springer
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Автор: Hester Bijl; Didier Lucor; Siddhartha Mishra; Chri
Название: Uncertainty Quantification in Computational Fluid Dynamics
ISBN: 3319346660 ISBN-13(EAN): 9783319346663
Издательство: Springer
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Описание: It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space.

Автор: Shi Jin; Lorenzo Pareschi
Название: Uncertainty Quantification for Hyperbolic and Kinetic Equations
ISBN: 331967109X ISBN-13(EAN): 9783319671093
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods.

Автор: Sullivan, T.J.
Название: Introduction to Uncertainty Quantification
ISBN: 3319233947 ISBN-13(EAN): 9783319233949
Издательство: Springer
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Цена: 8384.00 р.
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Описание: This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study. Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field.

Автор: Charles L. Webber, Jr.; Norbert Marwan
Название: Recurrence Quantification Analysis
ISBN: 3319071548 ISBN-13(EAN): 9783319071541
Издательство: Springer
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Цена: 13275.00 р.
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Описание: The analysis of recurrences in dynamical systems by using recurrence plots and their quantification is still an emerging field.

Автор: Charles L. Webber, Jr.; Cornel Ioana; Norbert Marw
Название: Recurrence Plots and Their Quantifications: Expanding Horizons
ISBN: 3319299212 ISBN-13(EAN): 9783319299211
Издательство: Springer
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Цена: 23508.00 р.
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Описание: The chapters in this book originate from the research work and contributions presented at the Sixth International Symposium on Recurrence Plots held in Grenoble, France in June 2015.

Автор: Jadamba
Название: Uncertainty Quantification In Varia
ISBN: 1138626325 ISBN-13(EAN): 9781138626324
Издательство: Taylor&Francis
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Описание: The primary objective of this book is to present a comprehensive treatment of uncertainty quantification in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields.

Автор: Biegler
Название: Large-Scale Inverse Problems and Quantification of Uncertainty
ISBN: 0470697431 ISBN-13(EAN): 9780470697436
Издательство: Wiley
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Цена: 17733.00 р.
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Описание: This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems.