Introduction to uncertainty quantification, Sullivan, T.j.
Автор: Olivier Le Maitre; Omar M Knio Название: Spectral Methods for Uncertainty Quantification ISBN: 9400731922 ISBN-13(EAN): 9789400731929 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Jadamba Название: Uncertainty Quantification In Varia ISBN: 1138626325 ISBN-13(EAN): 9781138626324 Издательство: Taylor&Francis Рейтинг: Цена: 16843.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Roger Ghanem; David Higdon; Houman Owhadi Название: Handbook of Uncertainty Quantification ISBN: 3319123858 ISBN-13(EAN): 9783319123851 Издательство: Springer Рейтинг: Цена: 181680.00 р. Наличие на складе: Поставка под заказ.
Автор: Sullivan, T.J. Название: Introduction to Uncertainty Quantification ISBN: 3319233947 ISBN-13(EAN): 9783319233949 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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
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.
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.
Автор: Olivier Le Maitre; Omar M Knio Название: Spectral Methods for Uncertainty Quantification ISBN: 9048135192 ISBN-13(EAN): 9789048135196 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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;.-
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
Автор: Hester Bijl; Didier Lucor; Siddhartha Mishra; Chri Название: Uncertainty Quantification in Computational Fluid Dynamics ISBN: 3319346660 ISBN-13(EAN): 9783319346663 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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