The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as well as to detect patterns in large-scale simulations or experimental tests. In order to interpret the relationships generated in the models during the training process, numerical attributions need to be assigned to the input features. One important example are the additive-feature-attribution methods. These explainability methods link the input features with the model prediction, providing an interpretation based on a linear formulation of the models. The SHapley Additive exPlanations (SHAP values) are formulated as the only possible interpretation that offers a unique solution for understanding the model. In this manuscript, the additive-feature-attribution methods are presented, showing four common implementations in the literature: kernel SHAP, tree SHAP, gradient SHAP, and deep SHAP. Then, the main applications of the additive-feature-attribution methods are introduced, dividing them into three main groups: turbulence modeling, fluid-mechanics fundamentals, and applied problems in fluid dynamics and heat transfer. This review shows thatexplainability techniques, and in particular additive-feature-attribution methods, are crucial for implementing interpretable and physics-compliant deep-learning models in the fluid-mechanics field.

翻译：近年来，数据驱动方法在流体力学中的应用急剧增长，这得益于其适应湍流复杂多尺度特性的能力，以及在大规模模拟或实验测试中识别模式的能力。为了解释模型在训练过程中生成的关系，需要为输入特征分配数值归因。加性特征归因方法便是一个重要范例。这类可解释性方法将输入特征与模型预测相关联，基于模型的线性公式提供解释。SHapley加性解释（SHAP值）被表述为唯一可能提供独特解以理解模型的解释方法。本文介绍了加性特征归因方法，展示了文献中四种常见实现：核SHAP、树SHAP、梯度SHAP与深度SHAP。随后，通过将主要应用分为三大类——湍流建模、流体力学基本原理、以及流体动力学与传热应用问题，系统阐述了加性特征归因方法的核心应用场景。本综述表明，可解释性技术（特别是加性特征归因方法）对于在流体力学领域构建可解释且符合物理规律的深度学习模型具有至关重要的作用。