Aerodynamic prediction for waveriders using deep residual learning

The applications of deep learning in aerodynamic predictions are explored in the article. A big data set is generated from 3D waverider design using the Latin Hypercube Sampling. The deep residual neural network (ResNet) is applied to construct a surrogate model for aerodynamic shape parameters and aerodynamic performances. Compared with the Random Forests and two-hidden-layer Neural Network, the high efficiency of ResNet is studied. In addition, the idea of surrogate models based on the image recognition is proposed with the image set. The aerodynamic performance prediction omits the shape parameterization, and the surrogate model is easy to be implemented. The efficiency of the ResNet surrogate model is over 3 times higher than that of the common machine learning methods, such as Random Forests and two-hidden-layer Neural Network. However, the efficiency of the ResNet image recognition model is not improved. The results suggest that the feasibility of the deep learning in aerodynamic shape design is confirmed, especially in some simple configurations, such as waverider.