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arXiv · 1906.09439

A support vector regression-based multi-fidelity surrogate model

Maolin Shi·Shuo Wang·Wei Sun·Liye Lv·Xueguan Song
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Abstract

Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming than an low-fidelity (LF) model. To take advantages of both HF and LF models, multi-fidelity surrogate models that aim to integrate information from both HF and LF models have gained increasing popularity. In this paper, a multi-fidelity surrogate model based on support vector regression named as Co_SVR is developed by combining HF and LF models. In Co_SVR, a kernel function is used to map the map the difference between the HF and LF models. Besides, a heuristic algorithm is used to obtain the optimal parameters of Co_SVR. The proposed Co_SVR is compared with two popular multi-fidelity surrogate models Co_Kriging model, Co_RBF model, and their single-fidelity surrogates through several numerical cases and a pressure vessel design problem. The results show that Co_SVR provides competitive prediction accuracy for numerical cases, and presents a better performance compared with the Co_Kriging and Co_RBF models and single-fidelity surrogate models.

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Maolin Shi, Shuo Wang, Wei Sun, Liye Lv, Xueguan Song. 2019-06-22. A support vector regression-based multi-fidelity surrogate model. https://doi.org/10.1007/s00158-020-02522-6

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