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Engineering and applied science rely on computational experiments torigorously study physical systems. The mathematical models used to probe thesesystems are highly complex, and sampling-intensive studies often requireprohibitively many simulations for acceptable accuracy. Surrogate modelsprovide a means of circumventing the high computational expense of samplingsuch complex models. In particular, polynomial chaos expansions (PCEs) havebeen successfully used for uncertainty quantification studies of deterministicmodels where the dominant source of uncertainty is parametric. We discuss anextension to conventional PCE surrogate modeling to enable surrogateconstruction for stochastic computational models that have intrinsic noise inaddition to parametric uncertainty. We develop a PCE surrogate on a joint spaceof intrinsic and parametric uncertainty, enabled by Rosenblatt transformations,and then extend the construction to random field data via the Karhunen-Loeveexpansion. We then take advantage of closed-form solutions for computing PCESobol indices to perform a global sensitivity analysis of the model whichquantifies the intrinsic noise contribution to the overall model outputvariance. Additionally, the resulting joint PCE is generative in the sense thatit allows generating random realizations at any input parameter setting thatare statistically approximately equivalent to realizations from the underlyingstochastic model. The method is demonstrated on a chemical catalysis examplemodel.

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty