TY  - JOUR
AU  - Schmidt, Raoul
AU  - Haehne, Hauke
AU  - Hillmann, Laura
AU  - Casadiego, Jose
AU  - Witthaut, Dirk
AU  - Schafer, Benjamin
AU  - Timme, Marc
TI  - Inferring topology of networks with hidden dynamic variablesnet
JO  - IEEE access
VL  - 10
SN  - 2169-3536
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2022-03221
SP  - 76682 - 76692
PY  - 2022
AB  - Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering. Most current inference methods rely on time series data recorded from all dynamical variables in the system. In applications, often only some of these time series are accessible, while other units or variables of all units are hidden, i.e. inaccessible or unobserved. For instance, in AC power grids, frequency measurements often are easily available whereas determining the phase relations among the oscillatory units requires much more effort. Here, we propose a network inference method that allows to reconstruct the full network topology even if all units exhibit hidden variables. We illustrate the approach in terms of a basic AC power grid model with two variables per node, the local phase angle and the local instantaneous frequency. Based solely on frequency measurements, we infer the underlying network topology as well as the relative phases that are inaccessible to measurement. The presented method may be enhanced to include systems with more complex coupling functions and additional parameters such as losses in power grid models. These results may thus contribute towards developing and applying novel network inference approaches in engineering, biology and beyond.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000831058200001
DO  - DOI:10.1109/ACCESS.2022.3191665
UR  - https://juser.fz-juelich.de/record/909515
ER  -