Learning aerodynamics with neural network

Abstract We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two orders of less parameters. We further investigate and explain how the ESCNN succeeds in making accurate predictions with standard convolution layers. We discover that the ESCNN has the ability to extract physical patterns that emerge from aerodynamics, and such patterns are clearly reflected within a layer of the network. We show that the ESCNN is capable of learning the physical laws and equation of aerodynamics from simulation data.