A support vector regression-based multi-fidelity surrogate model
This work addresses the need for efficient and accurate surrogate models in engineering design, but it is incremental as it builds on existing multi-fidelity methods with a new hybrid approach.
The paper tackled the problem of integrating high-fidelity and low-fidelity models in engineering design by developing a multi-fidelity surrogate model called Co_SVR based on support vector regression, which showed competitive prediction accuracy in numerical cases and outperformed existing multi-fidelity and single-fidelity models in a pressure vessel design problem.
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.