Smart Sensor Placement: A Correlation-Aware Attribution Framework (CAAF) for Real-world Data Modeling

2025-10-25

AI Summary

In complex real-world systems, highly correlated input features impede effective sensor placement optimization. Method: This paper proposes a correlation-aware feature attribution framework that first applies clustering-based preprocessing to decouple redundant, correlated features—thereby mitigating the failure of conventional attribution methods in chaotic, nonlinear dynamical environments—and subsequently integrates machine learning–based attribution with multiscale dynamic modeling. Contribution/Results: The framework achieves precise identification of critical sensor locations across diverse tasks, including structural health monitoring, aerodynamic lift prediction, and turbulent velocity estimation. Experiments demonstrate that it significantly outperforms traditional sensor deployment strategies, offering superior interpretability, generalizability, and cross-domain applicability. By explicitly accounting for feature correlation, the method establishes a novel paradigm for sensor optimization in high-correlation regimes.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex real-world systems. We propose a machine-learning-based feature attribution framework to identify OSP for the prediction of quantities of interest. Feature attribution quantifies input contributions to a model's output; however, it struggles with highly correlated input data often encountered in real-world applications. To address this, we propose a Correlation-Aware Attribution Framework (CAAF), which introduces a clustering step before performing feature attribution 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 real-world 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 feature attribution for identifying OSP in real-world environments.

Problem

• Addresses optimal sensor placement in complex real-world systems
• Solves feature attribution challenges with highly correlated input data
• Enables effective sensor placement for nonlinear dynamic systems

Innovation

• Clustering before attribution reduces feature redundancy
• Correlation-aware framework handles highly correlated input data
• Machine learning identifies optimal sensor placement locations