The aerodynamic characteristics are modeled based on machine learning

Aerodynamic characteristic modeling mainly includes mechanism modeling method and "black box" modeling method. This paper analyzes and applies the machine learning methods of "black box" modeling - Classification and regression tree method and shallow learning method. The classification and regression tree method, Kriging modeling method, RBF neural network method and SVM support vector machine method in shallow learning method are applied to rocket aerodynamic characteristic modeling, delta wing unsteady aerodynamic characteristic modeling at high angle of attack and aerodynamic thermal test data fusion respectively. The advantages and disadvantages of these modeling methods are compared and analyzed, and better prediction results are obtained, The application scope of machine learning modeling method for aerodynamic characteristics is expanded.