  Engineering and applied science rely on computational experiments to
rigorously study physical systems. The mathematical models used to probe these
systems are highly complex, and sampling-intensive studies often require
prohibitively many simulations for acceptable accuracy. Surrogate models
provide a means of circumventing the high computational expense of sampling
such complex models. In particular, polynomial chaos expansions (PCEs) have
been successfully used for uncertainty quantification studies of deterministic
models where the dominant source of uncertainty is parametric. We discuss an
extension to conventional PCE surrogate modeling to enable surrogate
construction for stochastic computational models that have intrinsic noise in
addition to parametric uncertainty. We develop a PCE surrogate on a joint space
of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations,
and then extend the construction to random field data via the Karhunen-Loeve
expansion. We then take advantage of closed-form solutions for computing PCE
Sobol indices to perform a global sensitivity analysis of the model which
quantifies the intrinsic noise contribution to the overall model output
variance. Additionally, the resulting joint PCE is generative in the sense that
it allows generating random realizations at any input parameter setting that
are statistically approximately equivalent to realizations from the underlying
stochastic model. The method is demonstrated on a chemical catalysis example
model.
