Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

• URL: http://arxiv.org/abs/2311.00553v2
• Date: Mon, 17 Jun 2024 19:18:37 GMT
• Title: Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty
• Authors: Joy N. Mueller, Khachik Sargsyan, Craig J. Daniels, Habib N. Najm,
• Abstract summary: Surrogate models provide a means of circumventing the high computational expense of complex models. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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