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Multivariate output global sensitivity analysis using multi-output support vector regression

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Abstract

Models with multivariate outputs are widely used for risk assessment and decision-making in practical applications. In this paper, multi-output support vector regression (M-SVR) is employed for global sensitivity analysis (GSA) with multivariate output models. The orthogonal polynomial kernel is used to build the M-SVR meta-model, and the covariance-based sensitivity indices of multivariate output are obtained analytically by post-processing the coefficients of M-SVR model. In order to improve the performance of the orthogonal polynomial kernel M-SVR model, a kernel function iteration algorithm is introduced further. The proposed method take advantage of the information of all outputs to get robust meta-model. To validate the performance of the proposed method, two high-dimensional analytical functions and a hydrological model (HYMOD) with multiple outputs are examined, and a detailed comparison is made with the sparse polynomial chaos expansion meta-model developed in UQLab Toolbox. Results show that the proposed methods are efficient and accurate for GSA of the complex multivariate output models.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. NSFC 51475370 and NSFC 51775439).

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Authors and Affiliations

  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Kai Cheng, Zhenzhou Lu & Kaichao Zhang

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  1. Kai Cheng
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  2. Zhenzhou Lu
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  3. Kaichao Zhang
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Correspondence to Zhenzhou Lu.

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Responsible editor: Pingfeng Wang

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Cheng, K., Lu, Z. & Zhang, K. Multivariate output global sensitivity analysis using multi-output support vector regression. Struct Multidisc Optim 59, 2177–2187 (2019). https://doi.org/10.1007/s00158-018-2184-z

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