Results 121 to 130 of about 78,109 (159)
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2017
This book results from a course developed by the author and reflects both his own and collaborative research regarding the development and implementation of uncertainty quantification (UQ) techniques for large-scale applications over the last two decades.The objectives of this book are to present fundamental notions for the stochastic modeling of ...
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This book results from a course developed by the author and reflects both his own and collaborative research regarding the development and implementation of uncertainty quantification (UQ) techniques for large-scale applications over the last two decades.The objectives of this book are to present fundamental notions for the stochastic modeling of ...
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2019
Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
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Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
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Model Uncertainty Quantification
2015Uncertainty quantification (UQ) is the analytic process of determining the effect of input uncertainties (both their magnitudes and sources) on system outcomes. Traditionally applied in engineering reliability analysis, UQ now plays a significant role in environmental and water resource (EWR) applications as environmental engineers and modelers are ...
Ne-Zheng Sun, Alexander Sun
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Objective Uncertainty Quantification
2019When designing an operator to alter the behavior of a physical system, the standard engineering paradigm is to begin with a scientific model describing the system, mathematically characterize a class of operators, define a performance cost relative to the operational objective, and pick an operator that minimizes the performance cost.
Edward R. Dougherty +2 more
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Uncertainty Quantification of Derivative Instruments
SSRN Electronic Journal, 2015AbstractModel and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being
Sun, Xianming, Vanmaele, Michèle
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Spectral analysis for uncertainty quantification
Numerical Linear Algebra with Applications, 2020AbstractDuring the past few decades, uncertainty quantification (UQ) techniques have been developed and applied to many applications. The majority of these techniques have been applied directly to specifically defined problems, that is, problems described by a mathematical operator and a specific source term, both which may be endowed with uncertainty.
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Uncertainty Quantifications for Multiviewcorrelation
The aim of this extended abstract is to introduce a general framework for the quantifications of uncertainties associated with displacement measurements via multiview correlation. The latter is an extension of stereocorrelation approaches to systems with more than two cameras.Hild, François, Roux, Stephane
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
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