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@ARTICLE{Schmidt:909515,
      author       = {Schmidt, Raoul and Haehne, Hauke and Hillmann, Laura and
                      Casadiego, Jose and Witthaut, Dirk and Schafer, Benjamin and
                      Timme, Marc},
      title        = {{I}nferring topology of networks with hidden dynamic
                      variablesnet},
      journal      = {IEEE access},
      volume       = {10},
      issn         = {2169-3536},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03221},
      pages        = {76682 - 76692},
      year         = {2022},
      abstract     = {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.},
      cin          = {IEK-STE},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / CoNDyNet 2 - Kollektive Nichtlineare Dynamik
                      Komplexer Stromnetze (BMBF-03EK3055B) / Verbundvorhaben
                      CoNDyNet: Systemanalytische Bewertung von Energiesicherheit
                      im Stromnetz (03SF0472B)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-JUEL1)BMBF-03EK3055B /
                      G:(BMBF)03SF0472B},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000831058200001},
      doi          = {10.1109/ACCESS.2022.3191665},
      url          = {https://juser.fz-juelich.de/record/909515},
}