%0 Journal Article %A Camporeale, Enrico %A Agnihotri, Ashutosh %A Rutjes, Casper %D 2017 %I Begell House %K adaptive sampling, hierarchical surplus, Clenshaw-Curtis %N 4 %P 285-301 %R 10.1615/Int.J.UncertaintyQuantification.2017020027 %T ADAPTIVE SELECTION OF SAMPLING POINTS FOR UNCERTAINTY QUANTIFICATION %U https://www.dl.begellhouse.com/journals/52034eb04b657aea,0ce170d9609cac4a,66cc3b4e23494a9a.html %V 7 %X We present a simple and robust strategy for the selection of sampling points in uncertainty quantification. The goal is to achieve the fastest possible convergence in the cumulative distribution function of a stochastic output of interest. We assume that the output of interest is the outcome of a computationally expensive nonlinear mapping of an input random variable, whose probability density function is known. We use a radial function basis to construct an accurate interpolant of the mapping. This strategy enables adding new sampling points one at a time, adaptively. This takes into full account the previous evaluations of the target nonlinear function. We present comparisons with a stochastic collocation method based on the Clenshaw-Curtis quadrature rule, and with an adaptive method based on hierarchical surplus, showing that the new method often results in a large computational saving. %8 2017-08-24