概率盒全局灵敏度和活跃子空间跨层降维

doi:10.7527/S1000-6893.2020.24582

Abstract

Abstract: Uncertainties in aerospace simulation systems are usually multi-sourced and mixed, with a high dimension of system parameters. To address the problem of mixed uncertainty quantification of high-dimension parameters, a cross-layer dimension reduction method combining the global sensitivity analysis of the probability box and the active subspace method is proposed. Based on the probability box characterization for aleatory and epistemic uncertainties, the pinching method is used to analyze the global sensitivity of parameters for parameter selection. The active subspace method is adopted for dimension reduction according to the eigen-decomposition of the output gradient covariance matrix. A cross-layer method based on the parameter selection and dimension reduction with the probability box analysis is then constructed. Finally, the NASA multidisciplinary uncertainty quantification challenge problem is taken as an example. The first-level parameter selection is performed through the global sensitivity analysis of the probability box, and the dimension of the input parameters is reduced from the original 21 to 13. Then the active subspace method is used for the second-level dimension reduction, with the dimension of parameters further reduced to one. The results demonstrate that the proposed method can perform sensitivity ranking for parameters with mixed uncertainties and effectively reduce the dimension of the model input parameters, laying a foundation for further mixed uncertainty quantification and optimization of high-dimension systems.

Key words: probability box, global sensitivity, pinching method, active subspace, dimension reduction, cross-layer

CLC Number:

V215

HU Zhengwen, ZHANG Baoqiang, DENG Zhenhong. Cross-layer dimension reduction based on probability box global sensitivity analysis and active subspace method[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(9): 224582-224582.

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Cross-layer dimension reduction based on probability box global sensitivity analysis and active subspace method

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