[2510.22517] Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)

Computer Science > Computational Engineering, Finance, and Science arXiv:2510.22517 (cs) [Submitted on 26 Oct 2025 (v1), last revised 3 Apr 2026 (this version, v2)] Title:Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF) Authors:Sze Chai Leung, Di Zhou, H. Jane Bae View a PDF of the paper titled Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF), by Sze Chai Leung and 2 other authors View PDF HTML (experimental) Abstract: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...