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Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems

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Abstract

In practical engineering and industry fields, complicated and correlated problems are often descripted by implicit expression. The Kriging model is one of the popular spatial interpolation models to surrogate the numerical relationship between input and output variables. But the efficiency of the Kriging surrogate model is limited when confronting with large databases. The subset simulation is a promising selection method to provide more important and typical samples. By the subset simulation, the Kriging surrogate model can significantly reduce the computational cost in regression, since much fewer samples are required. Besides, more reliable prediction results can be obtained because of the emphasis on the samples that are more representative in the Kriging fitting process. Examples are performed to confirm the properties of the Kriging surrogate model based on the subset simulation.

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Authors and Affiliations

  1. School of Transportation, Nantong University, Nantong, 226019, China

    Liu Chu & Jiajia Shi

  2. Département Mécanique, Institut National des Sciences Appliquées de Rouen, 76801, Rouen, France

    Eduardo Souza de Cursi

  3. State Grid Nantong Power Supply Company, Nantong, 226019, China

    Shujun Ben

Authors
  1. Liu Chu
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  2. Jiajia Shi
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  3. Eduardo Souza de Cursi
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  4. Shujun Ben
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Correspondence to Jiajia Shi.

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Communicated by Antonio José Silva Neto.

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Chu, L., Shi, J., Souza de Cursi, E. et al. Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems. Comp. Appl. Math. 39, 119 (2020). https://doi.org/10.1007/s40314-020-01147-1

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  • DOI: https://doi.org/10.1007/s40314-020-01147-1

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