Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attribution (FA) framework to identify OSP for target predictions. FA quantifies input contributions to a model output; however, it struggles with highly correlated input data often encountered in practical applications for OSP. To address this, we propose a Correlation-Assisted Attribution Framework (CAAF), which introduces a clustering step on the candidate sensor locations before performing FA to reduce redundancy and enhance generalizability. We first illustrate the core principles of the proposed framework through a series of validation cases, then demonstrate its effectiveness in realistic dynamical systems such as structural health monitoring, airfoil lift prediction, and wall-normal velocity estimation for turbulent channel flow. The results show that the CAAF outperforms alternative approaches that typically struggle due to the presence of nonlinear dynamics, chaotic behavior, and multi-scale interactions, and enables the effective application of FA for identifying OSP in real-world environments.

翻译：最优传感器布局(OSP)对于复杂物理系统中高效、精确的监测、控制与推理至关重要。我们提出一种基于机器学习的特征归因(FA)框架，用于识别面向目标预测的OSP。FA可量化输入对模型输出的贡献，但在处理实际OSP应用中常见的高度相关输入数据时存在局限。为此，我们提出相关性辅助归因框架(CAAF)，该方法在执行FA前对候选传感器位置进行聚类处理，以降低冗余性并增强泛化能力。首先通过系列验证案例阐述核心原理，继而展示其在结构健康监测、翼型升力预测、湍流槽道壁面法向速度估计等真实动力系统中的有效性。结果表明，CAAF优于因非线性动力学、混沌行为及多尺度相互作用而表现欠佳的传统方法，能够有效将FA应用于现实环境中的OSP识别。