A new sequential sampling method for constructing the high-order polynomial surrogate models

Jinglai Wu (School of Mechanical and Mechatronic Engineering, The University of Technology Sydney, Sydney, Australia)
Zhen Luo (School of Mechanical and Mechatronic Engineering, The University of Technology Sydney, Sydney, Australia)
Nong Zhang (School of Mechanical and Mechatronic Engineering, The University of Technology Sydney, Sydney, Australia)
Wei Gao (Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia)

Engineering Computations

ISSN: 0264-4401

Publication date: 16 April 2018

Abstract

Purpose

This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study.

Design/methodology/approach

The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated.

Findings

The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence.

Originality/value

The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.

Keywords

Citation

Wu, J., Luo, Z., Zhang, N. and Gao, W. (2018), "A new sequential sampling method for constructing the high-order polynomial surrogate models", Engineering Computations, Vol. 35 No. 2, pp. 529-564. https://doi.org/10.1108/EC-05-2016-0160

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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