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: Shi Jin; Lorenzo Pareschi Название: Uncertainty Quantification for Hyperbolic and Kinetic Equations ISBN: 331967109X ISBN-13(EAN): 9783319671093 Издательство: Springer Рейтинг: Цена: 13275.00 р. 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.
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.
Автор: Sullivan, T.j. Название: Introduction to uncertainty quantification ISBN: 3319794787 ISBN-13(EAN): 9783319794785 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.
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
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: 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;.-
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.
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: 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.
