Multi-fidelity aerodynamic data analysis by using composite neural network

Applying deep learning to aerodynamic data modeling has important practical significance. In this paper, the composite neural network is applied to the aerodynamics, making full use of the different characteristics of high and low-fidelity aerodynamic data. Multi-fidelity analysis technique is also used to analyze the correlation between the two types of data so as to establish the composite neural network. The experimental results show that the learning of multi-fidelity aerodynamic data based on the composite neural network model can better capture the mapping relationship between the aerodynamic input and the output data. And after comparing with the single neural network, it is verified that the present model has excellent performance in the regression modeling of aerodynamic data.