Neural network design for data-driven prediction of target geometry for an aerodynamic inverse design algorithm

With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness. Deep learning models enable us to exploit the data generated during those iterations to leverage the design algorithm. In this paper, design procedures and guidelines were presented for the use of multilayer feedforward neural network (MFNN) and long-short term memory (LSTM) network to predict the target geometry with early generated data of the design algorithm to reduce its computational cost. The impact of various parameters and hyperparameters on the quality of the target prediction was discussed and early results were presented for various representations of input data using the NACA-0011 airfoil aerodynamic design data. The results indicated that selecting the appropriate network and hyperparameters can yield a reliable estimate of the target geometry using only 20 % to 30 % of the available data.