Multifidelity uncertainty quantification with models based on dissimilar parameters

Zeng, Xiaoshu; Geraci, Gianluca; Eldred, Michael S.; Jakeman, John Davis; Gorodetsky, Alex A.; Ghanem, Roger

doi:10.1016/j.cma.2023.116205

Title: Multifidelity uncertainty quantification with models based on dissimilar parameters

Journal Article · Sat Aug 05 00:00:00 EDT 2023 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2023.116205· OSTI ID:2311523

^[1]; Geraci, Gianluca ^[2];

^[2]; Jakeman, John Davis ^[2];

^[3];

^[1]

Univ. of Southern California, Los Angeles, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Michigan, Ann Arbor, MI (United States)

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311523

Report Number(s):: SAND-2023-10096J

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Vol. 415; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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