A Stochastic Approach to the Synchronization of Coupled Oscillators
This paper deals with an optimal control problem associated with the Kuramoto model describing the dynamical behavior of a network of coupled oscillators. Our aim is to design a suitable control function allowing us to steer the system to a synchronized configuration in which all the oscillators are...
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doaj-f619030c1d6e4cff9f8d933b121e6fe42020-11-25T02:53:53ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2020-06-01810.3389/fenrg.2020.00115534163A Stochastic Approach to the Synchronization of Coupled OscillatorsUmberto Biccari0Umberto Biccari1Enrique Zuazua2Enrique Zuazua3Enrique Zuazua4Chair of Computational Mathematics, Fundación Deusto, Avda. de las Universidades 24, Bilbao, SpainUniversidad de Deusto, Avenida de las Universidades 24, Bilbao, SpainChair of Computational Mathematics, Fundación Deusto, Avda. de las Universidades 24, Bilbao, SpainChair in Applied Analysis, Alexander von Humboldt-Professorship, Department of Mathematics Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDepartamento de Matemáticas, Universidad Autónoma de Madrid, Madrid, SpainThis paper deals with an optimal control problem associated with the Kuramoto model describing the dynamical behavior of a network of coupled oscillators. Our aim is to design a suitable control function allowing us to steer the system to a synchronized configuration in which all the oscillators are aligned on the same phase. This control is computed via the minimization of a given cost functional associated with the dynamics considered. For this minimization, we propose a novel approach based on the combination of a standard Gradient Descent (GD) methodology with the recently-developed Random Batch Method (RBM) for the efficient numerical approximation of collective dynamics. Our simulations show that the employment of RBM improves the performances of the GD algorithm, reducing the computational complexity of the minimization process and allowing for a more efficient control calculation.https://www.frontiersin.org/article/10.3389/fenrg.2020.00115/fullcoupled oscillatorsKuramoto modeloptimal controlsynchronizationgradient descentrandom batch method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Umberto Biccari Umberto Biccari Enrique Zuazua Enrique Zuazua Enrique Zuazua |
spellingShingle |
Umberto Biccari Umberto Biccari Enrique Zuazua Enrique Zuazua Enrique Zuazua A Stochastic Approach to the Synchronization of Coupled Oscillators Frontiers in Energy Research coupled oscillators Kuramoto model optimal control synchronization gradient descent random batch method |
author_facet |
Umberto Biccari Umberto Biccari Enrique Zuazua Enrique Zuazua Enrique Zuazua |
author_sort |
Umberto Biccari |
title |
A Stochastic Approach to the Synchronization of Coupled Oscillators |
title_short |
A Stochastic Approach to the Synchronization of Coupled Oscillators |
title_full |
A Stochastic Approach to the Synchronization of Coupled Oscillators |
title_fullStr |
A Stochastic Approach to the Synchronization of Coupled Oscillators |
title_full_unstemmed |
A Stochastic Approach to the Synchronization of Coupled Oscillators |
title_sort |
stochastic approach to the synchronization of coupled oscillators |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Energy Research |
issn |
2296-598X |
publishDate |
2020-06-01 |
description |
This paper deals with an optimal control problem associated with the Kuramoto model describing the dynamical behavior of a network of coupled oscillators. Our aim is to design a suitable control function allowing us to steer the system to a synchronized configuration in which all the oscillators are aligned on the same phase. This control is computed via the minimization of a given cost functional associated with the dynamics considered. For this minimization, we propose a novel approach based on the combination of a standard Gradient Descent (GD) methodology with the recently-developed Random Batch Method (RBM) for the efficient numerical approximation of collective dynamics. Our simulations show that the employment of RBM improves the performances of the GD algorithm, reducing the computational complexity of the minimization process and allowing for a more efficient control calculation. |
topic |
coupled oscillators Kuramoto model optimal control synchronization gradient descent random batch method |
url |
https://www.frontiersin.org/article/10.3389/fenrg.2020.00115/full |
work_keys_str_mv |
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1724723805422616576 |