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Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering, Soize Christian


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Автор: Soize Christian
Название:  Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering
ISBN: 9783319853727
Издательство: Springer
Классификация:


ISBN-10: 3319853724
Обложка/Формат: Paperback
Страницы: 329
Вес: 0.49 кг.
Дата издания: 25.07.2018
Серия: Interdisciplinary applied mathematics
Язык: English
Издание: Softcover reprint of
Иллюстрации: 86 illustrations, color; 24 illustrations, black and white; xxii, 329 p. 110 illus., 86 illus. in color.
Размер: 23.39 x 15.60 x 1.85 cm
Читательская аудитория: General (us: trade)
Подзаголовок: An accelerated course with advanced applications in computational engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: An Accelerated Course with Applications in Computational Sciences and Engineering

Introduction to uncertainty quantification

Автор: Sullivan, T.j.
Название: Introduction to uncertainty quantification
ISBN: 3319794787 ISBN-13(EAN): 9783319794785
Издательство: Springer
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Цена: 55890.00 T
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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.

Uncertainty quantification and predictive computational science

Автор: Mcclarren, Ryan G.
Название: Uncertainty quantification and predictive computational science
ISBN: 3319995243 ISBN-13(EAN): 9783319995243
Издательство: Springer
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Цена: 93160.00 T
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Описание: This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties.

Computational Uncertainty Quantification for Inverse Problems

Автор: Johnathan M. Bardsley
Название: Computational Uncertainty Quantification for Inverse Problems
ISBN: 1611975379 ISBN-13(EAN): 9781611975376
Издательство: Mare Nostrum (Eurospan)
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Цена: 53090.00 T
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Описание: This book is an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The book also presents more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems. Each chapter contains MATLAB® code that implements the algorithms and generates the figures, as well as a large number of exercises accessible to both graduate students and researchers. Computational Uncertainty Quantification for Inverse Problems is intended for graduate students, researchers, and applied scientists. It is appropriate for courses on computational inverse problems, Bayesian methods for inverse problems, and UQ methods for inverse problems.

Uncertainty Quantification in Computational Fluid Dynamics

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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Цена: 102480.00 T
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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.

Uncertainty Quantification: Advances in Research and Applications

Автор: Luis Chase
Название: Uncertainty Quantification: Advances in Research and Applications
ISBN: 1536148628 ISBN-13(EAN): 9781536148626
Издательство: Nova Science
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Цена: 77080.00 T
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Описание: In recent times, polynomial chaos expansion has emerged as a dominant technique to determine the response uncertainties of a system by propagating the uncertainties of the inputs. In this regard, the opening chapter of Uncertainty Quantification: Advances in Research and Applications, an intrusive approach called Galerkin Projection as well as non-intrusive approaches (such as pseudo-spectral projection and linear regression) are discussed.Next, the authors introduce a new methodology to determine the uncertainties of input parameters using CIRCE software to overcome the reliance on expert judgment. The goal is to determinate and evaluate the uncertainty bounds for physical models related to reflood model of MARS-KS code Vessel module (coupled with COBRA-TF) using both CIRCE and the experimental data of FEBA.Lastly, uncertainties related to rheological model parameters of skeletal muscles are modeled and analyzed, and available data are acquired and fused for hyperelastic constitutive model parameters with Neo-Hookean and Mooney-Rivlin formulations.

Uncertainty Quantification for Hyperbolic and Kinetic Equations

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: 88500.00 T
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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.

Quantification of Uncertainty: Improving Efficiency and Technology: Quiet Selected Contributions

Автор: D`Elia Marta, Gunzburger Max, Rozza Gianluigi
Название: Quantification of Uncertainty: Improving Efficiency and Technology: Quiet Selected Contributions
ISBN: 3030487202 ISBN-13(EAN): 9783030487201
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs.

Handbook of Uncertainty Quantification

Автор: Roger Ghanem; David Higdon; Houman Owhadi
Название: Handbook of Uncertainty Quantification
ISBN: 3319123858 ISBN-13(EAN): 9783319123851
Издательство: Springer
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Цена: 1211200.00 T
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Spectral Methods for Uncertainty Quantification

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: 88500.00 T
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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.

Spectral Methods for Uncertainty Quantification

Автор: Olivier Le Maitre; Omar M Knio
Название: Spectral Methods for Uncertainty Quantification
ISBN: 9400731922 ISBN-13(EAN): 9789400731929
Издательство: Springer
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Цена: 79190.00 T
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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.

Multiscale Modeling and Uncertainty Quantification of Materials and Structures

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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Цена: 177010.00 T
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Model Validation and Uncertainty Quantification, Volume 3

Автор: Robert Barthorpe
Название: Model Validation and Uncertainty Quantification, Volume 3
ISBN: 3030120740 ISBN-13(EAN): 9783030120740
Издательство: Springer
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Цена: 186330.00 T
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Описание: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, the third volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:Inverse Problems and Uncertainty QuantificationControlling UncertaintyValidation of Models for Operating EnvironmentsModel Validation & Uncertainty Quantification: Decision MakingUncertainty Quantification in Structural DynamicsUncertainty in Early Stage DesignComputational and Uncertainty Quantification Tools


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