Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the coherent structures in the flow. Such interactions are explored in this study for the first time using an explainable deep-learning method. The instantaneous velocity field in a turbulent channel is used to predict the velocity field in time through a convolutional neural network. The predicted flow is used to assess the importance of each structure for this prediction using a game-theoretic algorithm (SHapley Additive exPlanations). This work provides results in agreement with previous observations in the literature and extends them by quantifying the importance of the Reynolds-stress structures, finding a causal connection between these structures and the dynamics of the flow. The process, based on deep-learning explainability, has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including the objective definition of new types of flow structures.

翻译：尽管壁面湍流具有重要的科学和技术意义，但它仍是一个未解决的问题，需要新的视角来攻克。其中的关键策略之一是研究流场中相干结构间的相互作用。本研究首次利用可解释的深度学习方法探索这些相互作用。通过卷积神经网络，以湍流通道中的瞬时速度场预测随时间演化的速度场。利用预测流场，通过博弈论算法（沙普利加法解释）评估各结构对该预测的重要性。研究结果与文献中先前观测一致，并通过量化雷诺应力结构的重要性加以延伸，揭示了这些结构与流场动力学之间的因果联系。基于深度学习可解释性的这一方法，具有阐明壁面湍流众多基本现象的潜力，包括新型流场结构的客观定义。