  Traditionally, deriving aerodynamic parameters for an airfoil via
Computational Fluid Dynamics requires significant time and effort. However,
recent approaches employ neural networks to replace this process, it still
grapples with challenges like lack of end-to-end training and interpretability.
A novel and more efficient neural network is proposed in this paper, called
AirfoilNet. AirfoilNet seamlessly merges mathematical computations with neural
networks, thereby augmenting interpretability. It encodes grey-scale airfoil
images into a lower-dimensional space for computation with Reynolds number,
angle of attack, and geometric coordinates of airfoils. The calculated features
are then fed into prediction heads for aerodynamic coefficient predictions, and
the entire process is end-to-end. Furthermore, two different prediction heads,
Gated Recurrent Unit Net(GRUNet) and Residual Multi-Layer Perceptron(ResMLP),
designed to support our iteratively refined prediction scheme. Comprehensive
analysis of experimental results underscores AirfoilNet's robust prediction
accuracy, generalization capability, and swift inference.
