  Compressive sampling has become a widely used approach to construct
polynomial chaos surrogates when the number of available simulation samples is
limited. Originally, these expensive simulation samples would be obtained at
random locations in the parameter space. It was later shown that the choice of
sample locations could significantly impact the accuracy of resulting
surrogates. This motivated new sampling strategies or design-of-experiment
approaches, such as coherence-optimal sampling, which aim at improving the
coherence property. In this paper, we propose a sampling strategy that can
identify near-optimal sample locations that lead to improvement in
local-coherence property and also enhancement of cross-correlation properties
of measurement matrices. We provide theoretical motivations for the proposed
sampling strategy along with several numerical examples that show that our
near-optimal sampling strategy produces substantially more accurate results,
compared to other sampling strategies.
