RT Journal Article ID 66cc3b4e23494a9a A1 Camporeale, Enrico A1 Agnihotri, Ashutosh A1 Rutjes, Casper T1 ADAPTIVE SELECTION OF SAMPLING POINTS FOR UNCERTAINTY QUANTIFICATION JF International Journal for Uncertainty Quantification JO IJUQ YR 2017 FD 2017-08-24 VO 7 IS 4 SP 285 OP 301 K1 adaptive sampling K1 hierarchical surplus K1 Clenshaw-Curtis AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,0ce170d9609cac4a,66cc3b4e23494a9a.html