Handbook of Uncertainty Quantification, Roger Ghanem; David Higdon; Houman Owhadi
Автор: Mcclarren, Ryan G. Название: Uncertainty quantification and predictive computational science ISBN: 3319995243 ISBN-13(EAN): 9783319995243 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Sullivan, T.j. Название: Introduction to uncertainty quantification ISBN: 3319794787 ISBN-13(EAN): 9783319794785 Издательство: Springer Рейтинг: Цена: 55890.00 T 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.
Автор: Robert Barthorpe Название: Model Validation and Uncertainty Quantification, Volume 3 ISBN: 3030120740 ISBN-13(EAN): 9783030120740 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Нет в наличии. 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: Robert Barthorpe Название: Model Validation and Uncertainty Quantification, Volume 3 ISBN: 3030090787 ISBN-13(EAN): 9783030090784 Издательство: Springer Рейтинг: Цена: 214280.00 T Наличие на складе: Нет в наличии. 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
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 Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: 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 Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: 112290.00 T 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: Shi Jin; Lorenzo Pareschi Название: Uncertainty Quantification for Hyperbolic and Kinetic Equations ISBN: 3030097900 ISBN-13(EAN): 9783030097905 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Нет в наличии. 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: 102480.00 T 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.
Автор: Shi Jin; Lorenzo Pareschi Название: Uncertainty Quantification for Hyperbolic and Kinetic Equations ISBN: 331967109X ISBN-13(EAN): 9783319671093 Издательство: Springer Рейтинг: 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.
Автор: Luis Chase Название: Uncertainty Quantification: Advances in Research and Applications ISBN: 1536148628 ISBN-13(EAN): 9781536148626 Издательство: Nova Science Рейтинг: Цена: 77080.00 T Наличие на складе: Невозможна поставка. 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.
