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Handbook of Uncertainty Quantification, Roger Ghanem; David Higdon; Houman Owhadi


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Автор: Roger Ghanem; David Higdon; Houman Owhadi
Название:  Handbook of Uncertainty Quantification
ISBN: 9783319123851
Издательство: Springer
Классификация:

ISBN-10: 3319123858
Вес: 0.00 кг.
Дата издания: 2017
Иллюстрации: 520 illus., 424 illus. in color. eReference.
Читательская аудитория: Science
Основная тема: Mathematics
Ссылка на Издательство: Link
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Поставляется из: Германии

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.

An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

Автор: Luis Tenorio
Название: An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems
ISBN: 1611974917 ISBN-13(EAN): 9781611974911
Издательство: Mare Nostrum (Eurospan)
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Цена: 61870.00 T
Наличие на складе: Невозможна поставка.
Описание: Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics.This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications.An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems includes:many examples that explain techniques which are useful to address general problems arising in uncertainty quantification; Bayesian and non-Bayesian statistical methods and discussions of their complementary roles; and analysis of a real data set to illustrate the methodology covered throughout the book.Audience: This book is intended for senior undergraduates and beginning graduate students in mathematics, engineering and physical sciences. The material spans from undergraduate statistics and probability to data analysis for inverse problems and probability distributions on infinite-dimensional spaces. It is also intended for researchers working on inverse problems and uncertainty quantification in geophysics, astrophysics, physics, and engineering. Because the statistical and probability methods covered have applications beyond inverse problems, the book may also be of interest to those people working in data science or in other applications of uncertainty quantification.

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.

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
Наличие на складе: Невозможна поставка.
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.

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
Наличие на складе: Поставка под заказ.
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

Model Validation and Uncertainty Quantification, Volume 3

Автор: Robert Barthorpe
Название: Model Validation and Uncertainty Quantification, Volume 3
ISBN: 3030090787 ISBN-13(EAN): 9783030090784
Издательство: Springer
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Цена: 214280.00 T
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Описание: Model Validation and Uncertainty Quantification, Volume 3:  Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics, 2018, the third volume of nine 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:Uncertainty Quantification in Material ModelsUncertainty Propagation in Structural DynamicsPractical Applications of MVUQAdvances in Model Validation & Uncertainty Quantification: Model UpdatingModel Validation & Uncertainty Quantification: Industrial ApplicationsControlling UncertaintyUncertainty in Early Stage DesignModeling of Musical InstrumentsOverview of Model Validation and Uncertainty

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th Imac, a Conference and Exposition on Structural Dynamics 2019

Автор: Barthorpe Robert
Название: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th Imac, a Conference and Exposition on Structural Dynamics 2019
ISBN: 3030120775 ISBN-13(EAN): 9783030120771
Издательство: Springer
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Цена: 186330.00 T
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Описание: 1.. Nondestructive Consolidation Assessment of Historical Camorcanna Ceilings by Scanning Laser Doppler Vibrometry;.- 2.. The Need for Credibility Guidance for Analyses Quantifying Margin and Uncertainty;.- 3.. Failure Behaviour of Composites under both Vibration and Environmental Temperature Loading Conditions;.- 4.. Verification and Validation for a Finite Element Model of a Hyperloop Pod Space Frame;.- 5.. Investigating Nonlinearities in a Demo Aircraft Structure under Sine Excitation;.- 6.. Sensor Placement for Multi-fidelity Dynamics Model Calibration;.- 7.. Application of Cumulative Prospect Theory to Optimal Inspection Decision-making for Ship Structures;.- 8.. Establishing an RMS von Mises Stress Error Bound for Random Vibration Analysis;.- 9.. A Neural Network Surrogate Model for Structural Health Monitoring of Miter Gates in Navigation Locks;.- 10.. Model Validation Strategy and Estimation of Response Uncertainty for a Bolted Structure with Model-form Errors;.- 11.. Characteristic Analysis of Dolly Rollover Test: A Study of effects of Initial Conditions on the Kinematics of the Vehicle and Occupants;.- 12.. Input Estimation of a Full-scale Concrete Frame Structure with Experimental Measurements;.- 13.. Bayesian Estimation of Acoustic Emission Arrival Times for Source Localization;.- 14.. Quantification and Evaluation of Parameter and Model Uncertainty for Passive and Active Vibration Isolation;.- 15.. Bayesian Model Updating of a Five-Story Building Using Zero-Variance Sampling Method;.- 16.. Input Estimation and Dimension Reduction for Material Models;.- 17.. Augmented Sequential Bayesian Filtering for Parameter and Modeling Error Estimation of Linear Dynamic Systems;.- 18.. On--board Monitoring of Rail Roughness via Axle box Accelerations of Revenue Trains with Uncertain Dynamics;.- 19.. Bayesian Identification of a Nonlinear Energy Sink Device: Method Comparison;.- 20.. Calibration of a Large Nonlinear Finite Element Model with Many Uncertain Parameters;.- 21.. Deep Unsupervised Learning For Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data;.- 22.. Influence of Furniture on the Modal Properties of Wooden Floors;.- 23.. Optimal Sensor Placement for Response Reconstruction in Structural Dynamics;.- 24.. Finite Element Model Updating Accounting for Modeling Uncertainty;.- 25.. Model-based Decision Support Methods Applied to the Conservation of Musical Instruments: Application to an Antique Cello;.- 26.. Optimal Sensor Placement for Response Predictions Using Local and Global Methods;.- 27.. Incorporating Uncertainty in the Physical Substructure during Hybrid Substructuring;.- 28.. Applying Uncertainty Quantification to Structural Systems: Parameter Reduction for Evaluating Model Complexity;.- 29.. Non-unique Estimates in Material Parameter Identification of Nonlinear FE Models Governed by Multiaxial Material Models Using Unscented Kalman Filter;.- 30.. On Key Technologies for Realising Digital Twins for Structural Dynamics Applications;.- 31.. Hygro‐mechanical Modelling of Wood and Glutin-based Bondlines of Wooden Cultural Heritage Objects;.- 32.. Modelling of Sympathetic String Vibrations in the Clavichord Using a Modal Udwadia-Kalaba Formulation;.- 33.. Modeling and Stochastic Dynamic Analysis of a Piezoelectric Shunted Rotating Beam;.- 34.. On Digital Twins, Mirrors and Virtualisations;.- 35.. Applications of Reduced Order and Surrogate Modeling in Structural Dynamics;.-

Uncertainty Quantification In Varia

Автор: Jadamba
Название: Uncertainty Quantification In Varia
ISBN: 1138626325 ISBN-13(EAN): 9781138626324
Издательство: Taylor&Francis
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Цена: 112290.00 T
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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.

Uncertainty Quantification for Hyperbolic and Kinetic Equations

Автор: Shi Jin; Lorenzo Pareschi
Название: Uncertainty Quantification for Hyperbolic and Kinetic Equations
ISBN: 3030097900 ISBN-13(EAN): 9783030097905
Издательство: Springer
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Цена: 102480.00 T
Наличие на складе: Поставка под заказ.
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.

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
Наличие на складе: Есть у поставщика Поставка под заказ.
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.

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

Автор: Soize Christian
Название: Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering
ISBN: 3319853724 ISBN-13(EAN): 9783319853727
Издательство: Springer
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Цена: 60550.00 T
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Описание: An Accelerated Course with Applications in Computational Sciences and Engineering

Modern Risk Quantification in Complex Projects: Non-Linear Monte Carlo and System Dynamics Methodologies

Автор: Raydugin Yuri G.
Название: Modern Risk Quantification in Complex Projects: Non-Linear Monte Carlo and System Dynamics Methodologies
ISBN: 0198844336 ISBN-13(EAN): 9780198844334
Издательство: Oxford Academ
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Цена: 189290.00 T
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Описание: Offers a general participation assessment across five levels of participation and cooperation and the specific behaviours that make up these five levels. The manual details instructions on how to administer the Social Profile, describes how the assessment was developed, and summarized research to support its use. Also included are 14 case studies that illustrate how the Social Profile can be used.


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