<ul><li>This paper demonstrates the use of Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to predict airfoil shapes from targeted pressure distribution and vice versa.</li><li>The dataset used in this study consists of 1600 airfoil shapes simulated at various Reynolds numbers and angles of attack.</li><li>The refined models show improved efficiency and reduced training time compared to the CNN model for complex datasets.</li><li>The proposed CNN and DNN models show promising results and have the potential to accelerate aerodynamic optimization and design of high-performance airfoils.</li></ul>

Accelerated Airfoil Design Using Neural Network Approaches

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