  We consider the problem of quantifying uncertainty regarding the output of an
electromagnetic field problem in the presence of a large number of uncertain
input parameters. In order to reduce the growth in complexity with the number
of dimensions, we employ a dimension-adaptive stochastic collocation method
based on nested univariate nodes. We examine the accuracy and performance of
collocation schemes based on Clenshaw-Curtis and Leja rules, for the cases of
uniform and bounded, non-uniform random inputs, respectively. Based on
numerical experiments with an academic electromagnetic field model, we compare
the two rules in both the univariate and multivariate case and for both
quadrature and interpolation purposes. Results for a real-world electromagnetic
field application featuring high-dimensional input uncertainty are also
presented.
