  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.
