A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design
ISSN: 0264-4401
Article publication date: 13 April 2023
Issue publication date: 19 April 2023
Abstract
Purpose
Extreme support vector regression (ESVR) has been widely used in the design, analysis and optimization of engineering systems of its fast training speed and good computational ability. However, the ESVR model is only able to utilize one-fidelity information of engineering system. To solve this issue, this paper extends extreme support vector regression (ESVR) to a multi-fidelity surrogate (MFS) model which can make use of a few expensive but higher-fidelity (HF) samples and a lot of inaccurate but cheap low-fidelity (LF) samples, named ESVR-MFS.
Design/methodology/approach
In the ESVR-MFS model, a kernel matrix is designed to evaluate the relationship between the HF and LF samples. The root mean square error of HF samples is used as the training error metric, and the optimal hyper-parameters of the kernel matrix are obtained through a heuristic algorithm.
Findings
A number of numerical problems and three engineering problems are used to compare the ESVR-MFS model with the single-fidelity ESVR model and two benchmark MFS models. The results show that the ESVR-MFS model exhibits competitive performance in both numerical cases and practical cases tested in this work.
Practical implications
The proposed approach exhibits great capability for practical multi-fidelity engineering design problems.
Originality/value
A MFS model is proposed based on ESVR, which can make full use of the advantages of both HF data and LF data to achieve optimal results at same or lower cost.
Keywords
Acknowledgements
This research is supported by the Natural Science Foundation of Jiangsu Province (BK20210777) and the Funding of Jiangsu University (20JDG068).
Citation
Shi, M.-L., Lv, L. and Xu, L. (2023), "A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design", Engineering Computations, Vol. 40 No. 2, pp. 473-493. https://doi.org/10.1108/EC-10-2021-0583
Publisher
:Emerald Publishing Limited
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