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Titlebook: Uncertainty Quantification in Scientific Computing; 10th IFIP WG 2.5 Wor Andrew M. Dienstfrey,Ronald F. Boisvert Conference proceedings 201

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Defects, Scientific Computation and the Scientific Methodly, unlike the experimental sciences, it does not adhere well to the principles of the scientific method as espoused by for example, the philosopher Karl Popper. Such principles are built around the notions of deniability and reproducibility. Although much research effort has been spent on measuring
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Using Emulators to Estimate Uncertainty in Complex Models are statistical descriptions of our beliefs about the models (or simulators). They can also be thought of as interpolators of simulator outputs between previous runs. Because they are quick to run, emulators can be used to carry out calculations that would otherwise require large numbers of simulat
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Numerical Aspects in the Evaluation of Measurement Uncertaintyry output is often an approximation to the PDF (probability density function) for the univariate or multivariate measurand (the quantity intended to be measured). All results of interest can be derived from this PDF. We consider uncertainty elicitation, propagation of distributions through a computa
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Model-Based Interpolation, Prediction, and Approximationably be entertained for each of these tasks, and the results are correspondingly varied, this often is a considerable source of uncertainty. Several statistical methods are illustrated that can be used to assess the contribution that this uncertainty component makes to the uncertainty budget: when i
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From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approachesns having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those
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