TY  - JOUR
A1  - Gout, Julien
A1  - Quade, Markus
A1  - Shafi, Kamran
A1  - Niven, Robert K.
A1  - Abel, Markus
T1  - Synchronization control of oscillator networks using symbolic regression
JF  - Nonlinear Dynamics
N2  - 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.
KW  - Dynamical systems
KW  - Synchronization control
KW  - Genetic programming
Y1  - 2017
U6  - https://doi.org/10.1007/s11071-017-3925-z
SN  - 0924-090X
SN  - 1573-269X
VL  - 91
IS  - 2
SP  - 1001
EP  - 1021
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - GEN
A1  - Waldrip, Steven H.
A1  - Niven, Robert K.
A1  - Abel, Markus
A1  - Schlegel, Michael
T1  - Consistent maximum entropy representations of pipe flow networks
T2  - AIP conference proceedings
N2  - 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.
Y1  - 2017
SN  - 978-0-7354-1527-0
U6  - https://doi.org/10.1063/1.4985365
SN  - 0094-243X
VL  - 1853
IS  - 1
PB  - American Institute of Physics
CY  - Melville
ER  - 
TY  - GEN
A1  - Waldrip, Steven H.
A1  - Niven, Robert K.
A1  - Abel, Markus
A1  - Schlegel, Michael
T1  - Maximum entropy analysis of transport networks
T2  - AIP conference proceedings
N2  - 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.
Y1  - 2017
SN  - 978-0-7354-1527-0
U6  - https://doi.org/10.1063/1.4985364
SN  - 0094-243X
VL  - 1853
IS  - 1
PB  - American Institute of Physics
CY  - Melville
ER  - 
TY  - JOUR
A1  - Waldrip, S. H.
A1  - Niven, Robert K.
A1  - Abel, Markus
A1  - Schlegel, M.
T1  - Reduced-Parameter Method for Maximum Entropy Analysis of Hydraulic Pipe Flow Networks
JF  - Journal of hydraulic engineering
N2  - 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.
KW  - Maximum entropy method
KW  - Water distribution systems
KW  - Hydraulic networks
KW  - Pipe networks
KW  - Hydraulic models
KW  - Nonlinear analysis
KW  - Probability
Y1  - 2017
U6  - https://doi.org/10.1061/(ASCE)HY.1943-7900.0001379
SN  - 0733-9429
SN  - 1943-7900
VL  - 144
IS  - 2
PB  - American Society of Civil Engineers
CY  - Reston
ER  -