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 C 3 2 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 C 3 2 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 C 3 2 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 C n 2 method when n = 4 and n = 5, providing a reference for further research on the combined modeling method.