Titlebook: Handbook of Uncertainty Quantification; Roger Ghanem,David Higdon,Houman Owhadi Living reference work 2020Latest edition Polynomial Chaos

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Conceptual Structure of Performance Assessments for Geologic Disposal of Radioactive Waste,e following three basic conceptual entities is described: EN1, a probability space that characterizes aleatory uncertainty; EN2, a function that predicts consequences for individual elements of the sample space for aleatory uncertainty; and EN3, a probability space that characterizes epistemic uncer

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COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management,enomena can be studied only by using computational processes such as complex simulations or analysis of experimental data. In addition, in many engineering fields computational approaches and virtual prototypes are used to support and drive the design of new components, structures, and systems. One

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Decision Analytic and Bayesian Uncertainty Quantification for Decision Support,coherently about uncertainty. According to Cox’s theorem, it is the . way to reason coherently about uncertainty. Probability summarizes states of information. A basic desideratum is that states of information judged equivalent should lead to the same probability distributions. Some widely used prob

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Derivative-Based Global Sensitivity Measures,s screening method and Sobol’ sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol’ sensitivity indices. This pape

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Design of Experiments for Screening,hose especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model

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