{"ID":2849939,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22517","arxiv_id":"2510.22517","title":"Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)","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 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.","short_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 wit...","url_abs":"https://arxiv.org/abs/2510.22517","url_pdf":"https://arxiv.org/pdf/2510.22517v2","authors":"[\"Sze Chai Leung\",\"Di Zhou\",\"H. Jane Bae\"]","published":"2025-10-26T03:50:16Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}