Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise

• URL: http://arxiv.org/abs/2312.11324v1
• Date: Mon, 18 Dec 2023 16:19:07 GMT
• Title: Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise
• Authors: Augusto Santos, Diogo Rente, Rui Seabra, Jos\'e M. F. Moura
• Abstract summary: In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes. The underlying network is unknown in many applications and should be inferred from observed data.
• Score: 2.362288417229025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes. The global dynamics naturally builds on this network of couplings and it is often excited by a noise input with nontrivial structure. The underlying network is unknown in many applications and should be inferred from observed data. We assume: i) Partial observability -- time series data is only available over a subset of the nodes; ii) Input noise -- it is correlated across distinct nodes while temporally independent, i.e., it is spatially colored. We present a feasibility condition on the noise correlation structure wherein there exists a consistent network inference estimator to recover the underlying fundamental dependencies among the observed nodes. Further, we describe a structure identification algorithm that exhibits competitive performance across distinct regimes of network connectivity, observability, and noise correlation.

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