  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.
