2024/09/11 by Rajasekhar Anguluri, Anguluri, Rajasekhar
Decision Sciences · Engineering · #Applications (stat.AP) #Dam Engineering and Safety #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Social and Information Networks (cs.SI) #Structural Health Monitoring Techniques
paper · pdf · doi:10.48550/arxiv.2409.08304
openalex publication_date 2024/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Adversarial actions and a rapid climate change are disrupting operations of infrastructure networks (e.g., energy, water, and transportation systems). Unaddressed disruptions lead to system-wide shutdowns, emphasizing the need for quick and robust identification methods. One significant disruption arises from edge changes (addition or deletion) in networks. We present an ℓ1-norm regularized least-squares framework to identify multiple but sparse edge changes using noisy data. We focus only on networks that obey equilibrium equations, as commonly observed in the above sectors. The presence or lack of edges in these networks is captured by the sparsity pattern of the weighted, symmetric Laplacian matrix, while noisy data are node injections and potentials. Our proposed framework systematically leverages the inherent structure within the Laplacian matrix, effectively avoiding overparameterization. We demonstrate the robustness and efficacy of the proposed approach through a series of representative examples, with a primary emphasis on power networks.