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A unified approach for global sensitivity analysis based on active subspace and Kriging

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  • Zhou, Changcong
  • Shi, Zhuangke
  • Kucherenko, Sergei
  • Zhao, Haodong

Abstract

A global sensitivity analysis (GSA) approach based on the theory of active subspaces and Kriging surrogate metamodeling is developed. Three GSA measures, namely the derivative-based global sensitivity measure (DGSM), activity score and Sobol’ total effect indices can be obtained at different steps of the proposed approach. Firstly, by estimating the covariance-like matrix C of response gradients values of DGSM are obtained. Then, the active subspace is identified by using the eigenvalue decomposition of the matrix C and obtained eigenvalues are used to estimate the activity score. In the active subspace, the Kriging model is built by an adaptive process. It is further used for estimation of Sobol’ total effect indices. Several test cases are examined to demonstrate the applicability of the proposed approach. They show how it provides different aspects of information from a single set of model runs. The proposed approach is then applied to a radome structure in fiber reinforced composites to find the most significant inputs for its safety.

Suggested Citation

  • Zhou, Changcong & Shi, Zhuangke & Kucherenko, Sergei & Zhao, Haodong, 2022. "A unified approach for global sensitivity analysis based on active subspace and Kriging," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021005780
    DOI: 10.1016/j.ress.2021.108080
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    Cited by:

    1. Vuillod, Bruno & Montemurro, Marco & Panettieri, Enrico & Hallo, Ludovic, 2023. "A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Shang, Xiaobing & Wang, Lipeng & Fang, Hai & Lu, Lingyun & Zhang, Zhi, 2024. "Active Learning of Ensemble Polynomial Chaos Expansion Method for Global Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    3. Feng, Kaixuan & Lu, Zhenzhou & Yang, Yixin & Ling, Chunyan & He, Pengfei & Dai, Ying, 2023. "Novel Kriging based learning function for system reliability analysis with correlated failure modes," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Buchwald, J. & Kolditz, O. & Nagel, T., 2024. "Design-of-Experiment (DoE) based history matching for probabilistic integrity analysis—A case study of the FE-experiment at Mont Terri," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Barr, John & Rabitz, Herschel, 2023. "Kernel-based global sensitivity analysis obtained from a single data set," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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