Paper Title
Inverse Airfoil Design Using Deep Learning

The task of determining the appropriate airfoil section for designing wings of airplanes or turbine blades has historically been a difficult and time-consuming process, requiring numerous design optimizations and simulations. However, the use of deep learning models such as Autoencoders and Multi-layer Perceptron (MLPs) presents a promising alternative by significantly reducing computation time and power required. In this study, these models are used to parameterize airfoils. By training them with data generated using JAVAFOIL, the models can generate new airfoils that correspond to previously unseen design parameters. The use of Convolutional Autoencoders (CAE) further reduces computation time and power during offline stages, allowing for the mapping of airfoil sections at various angles of attack to their corresponding design parameters, such as Reynolds number and coefficients of lift, drag, moment, and pressure. Keywords - Airfoil, Deep Learning, Multi-Layer Perceptron, Autoencoders