An inverse design method of airfoil based on deep learning was proposed

and the pressure distribution image and airfoil curve were taken as the learning objects to build the prediction model. It can obtain the airfoil curve by using convolutional neural network to extract geometric features from pressure distribution image

while avoiding the time-consuming process of traditional numerical calculation method. In the test case

6 000 samples of pressure distribution images and airfoil curves were used to train the prediction model

and the other 561 samples were used for validation with validation time only 67 s. The average relative error between the predicted and CFD results was 055%. In the comparison test

noise was added to the pressure distribution curve and the output layer size was changed to further validate the performance of the model. The results show that the proposed inverse design method of airfoil has strong robustness and prediction accuracy. The method can greatly reduce the computation time and improve the design efficiency of airfoil with satisfactory design accuracy.