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A novel surrogate for extremes of random functions

Author

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  • Xu, Hui
  • Grigoriu, Mircea D.
  • Gurley, Kurtis R.

Abstract

Numerical solutions of stochastic problems require the representation of random functions in their definitions by finite dimensional (FD) models, i.e., deterministic functions of time and finite sets of random variables. It is common to represent the coefficients of these FD surrogates by polynomial chaos (PC) models. We propose a novel model, referred to as the polynomial chaos translation (PCT) model, which matches exactly the marginal distributions of the FD coefficients and approximately their dependence. PC- and PCT-based FD models are constructed for a set of test cases and a wind pressure time series recorded at the boundary layer wind tunnel facility at the University of Florida. The PCT-based models capture the joint distributions of the FD coefficients and the extremes of target times series accurately while PC-based FD models do not have this capability.

Suggested Citation

  • Xu, Hui & Grigoriu, Mircea D. & Gurley, Kurtis R., 2023. "A novel surrogate for extremes of random functions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004076
    DOI: 10.1016/j.ress.2023.109493
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    References listed on IDEAS

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    1. Mara, Thierry A. & Becker, William E., 2021. "Polynomial chaos expansion for sensitivity analysis of model output with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    2. Liu, Zicheng & Lesselier, Dominique & Sudret, Bruno & Wiart, Joe, 2020. "Surrogate modeling based on resampled polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Lim, HyeongUk & Manuel, Lance, 2021. "Distribution-free polynomial chaos expansion surrogate models for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Zhang, Ruijing & Dai, Hongzhe, 2022. "A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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