Learning aerodynamics with neural network.

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.