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Uncertainty Quantification and Model Calibration

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Uncertainty Quantification

, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy;

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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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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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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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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 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 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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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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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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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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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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Uncertainty, Calibration and Probability

certain range. The more precise the measurement, the smaller the range of uncertainty. Uncertainty, Calibration and Probability is a comprehensive;

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High-order Adjoint Sensitivity Analysis Methodology for Large-scale Nonlinear Systems

uncertainty analysis * Applies to all fields involving numerical modeling, optimization, quantification of sensitivities in direct and inverse;

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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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Uncertainty Quantification Techniques in Statistics

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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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Introduction to Uncertainty Quantification

This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems includes: many;

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Scientific Computing in Electrical Engineering

devices, electromagnetic fields, and coupled problems. The focus in methodology was on model order reduction and uncertainty quantification;

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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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Inverse Problems

. The computational methods include nonsmooth optimization algorithms, direct inversion methods and uncertainty quantification via Bayesian;

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Traceable Temperatures

sensor is that of traceable calibration - a concept that has been developed to ensure that all measurements made are accurate and legally valid;

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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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