Titlebook: Quantification of Uncertainty: Improving Efficiency and Technology; QUIET selected contr Marta D‘Elia,Max Gunzburger,Gianluigi Rozza Book 2

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Tinea-Capitis

A Compressive Spectral Collocation Method for the Diffusion Equation Under the Restricted Isometry n a spectral Sturm-Liouville approximation of the solution and on the collocation of the PDE in strong form at randomized points, by taking advantage of the compressive sensing principle. The proposed approach makes use of a number of collocation points substantially less than the number of basis fu

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人造

Piecewise Polynomial Approximation of Probability Density Functions with Application to Uncertaintyt to a binning of the sample space. The kernel functions are a compactly supported basis for the space of such polynomials, i.e. finite element hat functions, that are centered at the bin nodes rather than at the samples, as is the case for the standard kernel density estimation approach. This featu

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Analysis of Probabilistic and Parametric Reduced Order Models,odels there is a notion of weak distribution or generalised random variable, and the same arguments can be used to analyse parametric models. Such models in vector spaces are connected to a linear map, and in infinite dimensional spaces are a true generalisation. Reproducing kernel Hilbert space and

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patriot

Uncertainty Quantification Applied to Hemodynamic Simulations of Thoracic Aorta Aneurysms: Sensitivivity analysis of numerical predictions to the shape of the inlet flow rate waveform is carried out. In particular, two parameters are selected to describe the inlet waveform: the stroke volume and the period of the cardiac cycle. In order to limit the number of hemodynamic simulations required, we

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