  Effectively predicting transonic unsteady flow over an aerofoil poses
inherent challenges. In this study, we harness the power of deep neural network
(DNN) models using the attention U-Net architecture. Through efficient training
of these models, we achieve the capability to capture the complexities of
transonic and unsteady flow dynamics at high resolution, even when faced with
previously unseen conditions. We demonstrate that by leveraging the
differentiability inherent in neural network representations, our approach
provides a framework for assessing fundamental physical properties via global
instability analysis. This integration bridges deep neural network models and
traditional modal analysis, offering valuable insights into transonic flow
dynamics and enhancing the interpretability of neural network models in
flowfield diagnostics.
