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.

翻译：有效预测翼型上的跨音速非定常流场面临固有挑战。本研究利用基于注意力U-Net架构的深度神经网络模型，通过高效训练，使模型能够在面对前所未见的工况时，高分辨率捕捉跨音速与非定常流动动力学的复杂性。我们证明，通过利用神经网络表示固有的可微性，该方法为通过全局不稳定性分析评估基本物理特性提供了框架。这一整合将深度神经网络模型与传统模态分析相结合，为跨音速流动力学提供了重要见解，并增强了神经网络模型在流场诊断中的可解释性。