@article{GoutQuadeShafietal.2017,
  author    = {Gout, Julien and Quade, Markus and Shafi, Kamran and Niven, Robert K. and Abel, Markus},
  title     = {Synchronization control of oscillator networks using symbolic regression},
  series = {Nonlinear Dynamics},
  volume    = {91},
  journal   = {Nonlinear Dynamics},
  number    = {2},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0924-090X},
  doi       = {10.1007/s11071-017-3925-z},
  pages     = {1001 -- 1021},
  year      = {2017},
  abstract  = {Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems.},
  language  = {en}
}
@misc{WaldripNivenAbeletal.2017,
  author    = {Waldrip, Steven H. and Niven, Robert K. and Abel, Markus and Schlegel, Michael},
  title     = {Consistent maximum entropy representations of pipe flow networks},
  series = {AIP conference proceedings},
  volume    = {1853},
  journal   = {AIP conference proceedings},
  number    = {1},
  publisher = {American Institute of Physics},
  address   = {Melville},
  isbn      = {978-0-7354-1527-0},
  issn      = {0094-243X},
  doi       = {10.1063/1.4985365},
  year      = {2017},
  abstract  = {The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.},
  language  = {en}
}
@misc{WaldripNivenAbeletal.2017,
  author    = {Waldrip, Steven H. and Niven, Robert K. and Abel, Markus and Schlegel, Michael},
  title     = {Maximum entropy analysis of transport networks},
  series = {AIP conference proceedings},
  volume    = {1853},
  journal   = {AIP conference proceedings},
  number    = {1},
  publisher = {American Institute of Physics},
  address   = {Melville},
  isbn      = {978-0-7354-1527-0},
  issn      = {0094-243X},
  doi       = {10.1063/1.4985364},
  pages     = {8},
  year      = {2017},
  abstract  = {The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made.},
  language  = {en}
}
@article{WaldripNivenAbeletal.2017,
  author    = {Waldrip, S. H. and Niven, Robert K. and Abel, Markus and Schlegel, M.},
  title     = {Reduced-Parameter Method for Maximum Entropy Analysis of Hydraulic Pipe Flow Networks},
  series = {Journal of hydraulic engineering},
  volume    = {144},
  journal   = {Journal of hydraulic engineering},
  number    = {2},
  publisher = {American Society of Civil Engineers},
  address   = {Reston},
  issn      = {0733-9429},
  doi       = {10.1061/(ASCE)HY.1943-7900.0001379},
  pages     = {10},
  year      = {2017},
  abstract  = {A maximum entropy (MaxEnt) method is developed to predict flow rates or pressure gradients in hydraulic pipe networks without sufficient information to give a closed-form (deterministic) solution. This methodology substantially extends existing deterministic flow network analysis methods. It builds on the MaxEnt framework previously developed by the authors. This study uses a continuous relative entropy defined on a reduced parameter set, here based on the external flow rates. This formulation ensures consistency between different representations of the same network. The relative entropy is maximized subject to observable constraints on the mean values of a subset of flow rates or potential differences, the frictional properties of each pipe, and physical constraints arising from Kirchhoff's first and second laws. The new method is demonstrated by application to a simple one-loop network and a 1,123-node, 1,140-pipe water distribution network in the suburb of Torrens, Australian Capital Territory, Australia.},
  language  = {en}
}