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Topics in Model Validation and Uncertainty Quantification, Volume 5

Topics in Model Validation and Uncertainty Quantification, Volume : Proceedings of the 31st IMAC, A Conference and Exposition on Structural;

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Model Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural;

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Model Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics;

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Model Validation and Uncertainty Quantification, Volume 3

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics;

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Assessing the Reliability of Complex Models

1 Introduction 2 Sources of Uncertainty and Error 3 Verification 4 Emulation, Reduced-Order Modeling, and Forward Propagation 5 Model;

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Uncertainty Quantification

The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties;

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Uncertainty Quantification and Model Calibration

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Uncertainty Quantification in Laminated Composites

Over the last few decades, uncertainty quantification in composite materials and structures has gained a lot of attention from the research;

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Uncertainty Quantification in Laminated Composites

Over the last few decades, uncertainty quantification in composite materials and structures has gained a lot of attention from the research;

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Uncertainty Quantification and Predictive Computational Science

, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources;

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Finite Element Analysis - Method, Verification and Validation, Second Edition

foundations of the finite element method and highlights the fundamentals of solution verification, validation, and uncertainty quantification. Written;

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Optimization Under Uncertainty with Applications to Aerospace Engineering

uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The;

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Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling

This volume deals with the pressing issue of uncertainty in archaeological modeling. Detecting where and when uncertainty is introduced;

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Probabilistic Risk Analysis

to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on;

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Uncertainty Quantification for Hyperbolic and Kinetic Equations

This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a;

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Uncertainty in Engineering

Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on;

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Uncertainty Quantification

quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non;

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Quantification of Uncertainty

reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context;

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Quantifying Uncertainty in Subsurface Systems

that influ ence models and decisions. Next, an overview of the various data science methods relevant to uncertainty quantification in the;

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Experimentation, Validation, and Uncertainty Analysis for Engineers

Helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations Fully;

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Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

) of the AFM (French Society of Mechanical Sciences), the Scientific Committee on Stochastic Modeling and Uncertainty Quantification of the ABCM;

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Managing Uncertainty, Mitigating Risk

possible, but incorporating uncertainty. The author shows that by using broad quantification techniques, and using reason as the guiding principle;

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Sub-structure Coupling for Dynamic Analysis

optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large;

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Sub-structure Coupling for Dynamic Analysis

optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large;

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Reviews in Computational Chemistry, Volume 31

on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and;

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Groundwater Models for Resources Analysis and Management

resources. Topics covered include model vs. data uncertainty, accuracy of the dispersion/convection equation, protocols for model testing and;

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Forecasting In The Presence Of Structural Breaks And Model Uncertainty

Forecasting in the presence of structural breaks and model uncertainty are active areas of recent research with crucial implications for;

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