A combined modeling method for complex multi-fidelity data fusion

Currently, mainstream methods for multi-fidelity data fusion have achieved great success in many fields, but they generally suffer from poor scalability. Therefore, this paper proposes a combination modeling method for complex multi-fidelity data fusion, devoted to solving the modeling problems with three types of multi-fidelity data fusion, and explores a general solution for any n types of multi-fidelity data fusion. Different from the traditional direct modeling method—Multi-Fidelity Deep Neural Network (MFDNN)—the method is an indirect modeling method. The experimental results on three representative benchmark functions and the prediction tasks of SG6043 airfoil aerodynamic performance show that combination modeling has the following advantages: (1) It can quickly establish the mapping relationship between high, medium, and low fidelity data. (2) It can effectively solve the data imbalance problem in multi-fidelity modeling. (3) Compared with MFDNN, it has stronger noise resistance and higher prediction accuracy. Additionally, this paper discusses the scalability problem of the method when n = 4 and n = 5, providing a reference for further research on the combined modeling method.