Complex Networks Of Oscillators Research Articles

Perfect synchronization in complex networks with higher-order interactions.

Achieving perfect synchronization in a complex network, specially in the presence of higher-order interactions (HOIs) at a targeted point in the parameter space, is an interesting, yet challenging task. Here we present a theoretical framework to achieve the same under the paradigm of the Sakaguchi-Kuramoto (SK) model. We analytically derive a frequency set to achieve perfect synchrony at some desired point in a complex network of SK oscillators with higher-order interactions. Considering the SK model with HOIs on top of the scale-free, random, and small world networks, we perform extensive numerical simulations to verify the proposed theory. Numerical simulations show that the analytically derived frequency set not only provides stable perfect synchronization in the network at a desired point but also proves to be very effective in achieving a high level of synchronization around it compared to the other choices of frequency sets. The stability and the robustness of the perfect synchronization state of the system are determined using the low-dimensional reduction of the network and by introducing a Gaussian noise around the derived frequency set, respectively.

Optimal time-varying coupling function can enhance synchronization in complex networks.

In this paper, we propose a time-varying coupling function that results in enhanced synchronization in complex networks of oscillators. The stability of synchronization can be analyzed by applying the master stability approach, which considers the largest Lyapunov exponent of the linearized variational equations as a function of the network eigenvalues as the master stability function. Here, it is assumed that the oscillators have diffusive single-variable coupling. All possible single-variable couplings are studied for each time interval, and the one with the smallest local Lyapunov exponent is selected. The obtained coupling function leads to a decrease in the critical coupling parameter, resulting in enhanced synchronization. Moreover, synchronization is achieved faster, and its robustness is increased. For illustration, the optimum coupling function is found for three networks of chaotic Rössler, Chen, and Chua systems, revealing enhanced synchronization.

Explosive death transitions in complex networks of limit cycle and chaotic systems

The conditions for explosive death transitions in complex networks of oscillators having generalized network topology, coupled via mean-field diffusion, are derived analytically. The network behaviour is characterized using three order parameters that define the average amplitude of oscillations, the mean state and the fraction of dead oscillators. As the mean field coupling is changed adiabatically, the amplitude order parameter undergoes explosive death transitions and the nodes in the network collectively cease to oscillate. The transition points in the parameter space and the boundaries of amplitude death regime are derived analytically. Sub-critical Hopf bifurcations are shown to be responsible for amplitude death transition. Explosive death transitions are characterized by hysteresis on adiabatic change of the bifurcation parameter and surprisingly, the backward transition point is shown to be independent of network topology. The theoretical developments have been validated through numerical examples, involving networks of limit cycle systems — such as the Van der Pol oscillator and chaotic systems, such as the Rossler attractor for both random and small world networks.

Enhancing Synchronization by Optimal Correlated Noise.

From the flashes of fireflies to Josephson junctions and power infrastructure, networks of coupled phase oscillators provide a powerful framework to describe synchronization phenomena in many natural and engineered systems. Most real-world networks are under the influence of noisy, random inputs, potentially inhibiting synchronization. While noise is unavoidable, here we show that there exist optimal noise patterns which minimize desynchronizing effects and even enhance order. Specifically, using analytical arguments we show that in the case of a two-oscillator model, there exists a sharp transition from a regime where the optimal synchrony-enhancing noise is perfectly anticorrelated, to one where the optimal noise is correlated. More generally, we then use numerical optimization methods to demonstrate that there exist anticorrelated noise patterns that optimally enhance synchronization in large complex oscillator networks. Our results may have implications in networks such as power grids and neuronal networks, which are subject to significant amounts of correlated input noise.

Finding nonlinear system equations and complex network structures from data: A sparse optimization approach.

In applications of nonlinear and complex dynamical systems, a common situation is that the system can be measured, but its structure and the detailed rules of dynamical evolution are unknown. The inverse problem is to determine the system equations and structure from time series. The principle of exploiting sparse optimization to find the equations of dynamical systems from data was first articulated in 2011 by the ASU group. The basic idea is to expand the system equations into a power series or a Fourier series of a finite number of terms and then to determine the vector of the expansion coefficients based solely on data through sparse optimization. This Tutorial presents a brief review of the recent progress in this area. Issues discussed include discovering the equations of stationary or nonstationary chaotic systems to enable the prediction of critical transition and system collapse, inferring the full topology of complex oscillator networks and social networks hosting evolutionary game dynamics, and identifying partial differential equations for spatiotemporal dynamical systems. Situations where sparse optimization works or fails are pointed out. The relation with the traditional delay-coordinate embedding method is discussed, and the recent development of a model-free, data-driven prediction framework based on machine learning is mentioned.

Synchronization of energy transmission networks at low voltage levels

Power grids or energy transmission networks are among the biggest and more complex man made constructions ever made and are a typical example of a complex system. Its components need to be in a synchronous state in order to be fully functional and avoid cascade failures and blackouts. Power grids can be modeled as a complex network of oscillators, where each node represents a generator or a consumer and the transmission lines are represented by edges. In this work, we show how to build a power grid topology that presents relatively low number of edges and favors synchronization as a low value of coupling is required to reach the synchronous state. As the coupling is related to the maximum transmission capacity of a transmission line, lower coupling in this context means lower voltage levels. The basin stability of this network is also calculated as it appears to have a higher quantity of stable nodes when compared to a random network. The methodology presented in this work is based on an evolutionary optimization framework and would be of great interest when building power grids due to the costs involved in the construction of transmission lines, as there would be less lines and they would be required to operate in a lower voltage level.

Disturbance Induced Synchronization in Networked Oscillatory System

The problem of synchronization of oscillators that are not coupled directly but are connected to a common disturbance has been studied in this work. Mathematical formalisms to obtain sufficient coupling gain for synchronization of a complex network of oscillators forced by a common disturbance have been developed. The Lyapunov stability approach using quadratic Lyapunov function and non-smooth Lyapunov function has been used for the purpose. The networked oscillatory systems considered are formed by Van der Pol and Fitzhugh Nagumo oscillators independently. These oscillators are diffusively coupled to common disturbance. The comparison of analytical approaches has been made, and the results are validated through numerical simulations.

The key player problem in complex oscillator networks and electric power grids: Resistance centralities identify local vulnerabilities.

Identifying key players in coupled individual systems is a fundamental problem in network theory. We investigate synchronizable network-coupled dynamical systems such as high-voltage electric power grids and coupled oscillators on complex networks. We define key players as nodes that, once perturbed, generate the largest excursion away from synchrony. A spectral decomposition of the coupling matrix gives an elegant solution to this identification problem. We show that, when the coupling matrix is Laplacian, key players are peripheral in the sense of a centrality measure defined from effective resistance distances. For linearly coupled systems, the ranking is efficiently obtained through a single Laplacian matrix inversion, regardless of the operational synchronous state. The resulting ranking index is termed LRank. When nonlinearities are present, a weighted Laplacian matrix inversion gives another ranking index, WLRank. LRank provides a faithful ranking even for well-developed nonlinearities, corresponding to oscillator angle differences up to approximately Δθ ≲ 40°.

Erratum: In-phase synchronization in complex oscillator networks by adaptive delayed feedback control [Phys. Rev. E 98 , 042302 (2018)

Erratum: In-phase synchronization in complex oscillator networks by adaptive delayed feedback control [Phys. Rev. E <b>98</b> , 042302 (2018)

In-phase synchronization in complex oscillator networks by adaptive delayed feedback control

In-phase synchronization is a special case of synchronous behavior when coupled oscillators have the same phases for any time moments. Such behavior appears naturally for nearly identical coupled limit-cycle oscillators when the coupling strength is greatly above the synchronization threshold. We investigate the general class of nearly identical complex oscillators connected into network in a context of a phase reduction approach. By treating each oscillator as a black-box possessing a single-input single-output, we provide a practical and simply realizable control algorithm to attain the in-phase synchrony of the network. For a general diffusive-type coupling law and any value of a coupling strength (even greatly below the synchronization threshold) the delayed feedback control with a specially adjusted time-delays can provide in-phase synchronization. Such adjustment of the delay times performed in an automatic fashion by the use of an adaptive version of the delayed feedback algorithm when time-delays become time-dependent slowly varying control parameters. Analytical results show that there are many arrangements of the time-delays for the in-phase synchronization, therefore we supplement the algorithm by an additional requirement to choose appropriate set of the time-delays, which minimize power of a control force. Performed numerical validations of the predictions highlights the usefulness of our approach.

Enhancing network synchronization by phase modulation.

Due to time delays in signal transmission and processing, phase lags are inevitable in realistic complex oscillator networks. Conventional wisdom is that phase lags are detrimental to network synchronization. Here we show that judiciously chosen phase lag modulations can result in significantly enhanced network synchronization. We justify our strategy of phase modulation, demonstrate its power in facilitating and enhancing network synchronization with synthetic and empirical network models, and provide an analytic understanding of the underlying mechanism. Our work provides an alternative approach to synchronization optimization in complex networks, with insights into control of complex nonlinear networks.

Model reduction for Kuramoto models with complex topologies.

Synchronization of coupled oscillators is a ubiquitous phenomenon, occurring in topics ranging from biology and physics to social networks and technology. A fundamental and long-time goal in the study of synchronization has been to find low-order descriptions of complex oscillator networks and their collective dynamics. However, for the Kuramoto model, the most widely used model of coupled oscillators, this goal has remained surprisingly challenging, in particular for finite-size networks. Here, we propose a model reduction framework that effectively captures synchronization behavior in complex network topologies. This framework generalizes a collective coordinates approach for all-to-all networks [G. A. Gottwald, Chaos 25, 053111 (2015)CHAOEH1054-150010.1063/1.4921295] by incorporating the graph Laplacian matrix in the collective coordinates. We first derive low dimensional evolution equations for both clustered and nonclustered oscillator networks. We then demonstrate in numerical simulations for Erdős-Rényi networks that the collective coordinates capture the synchronization behavior in both finite-size networks as well as in the thermodynamic limit, even in the presence of interacting clusters.

Selective control of vortex polarities by microwave field in two robustly synchronized spin-torque nano-oscillators

Manipulating operation states of coupled spin-torque nano-oscillators (STNOs), including their synchronization, is essential for applications such as complex oscillator networks. In this work, we experimentally demonstrate selective control of two coupled vortex STNOs through microwave-assisted switching of their vortex core polarities. First, the two oscillators are shown to synchronize due to the dipolar interaction in a broad frequency range tuned by an external biasing field. Coherent output is demonstrated along with strong linewidth reduction. Then, we show individual vortex polarity control of each oscillator, which leads to synchronization/desynchronization due to accompanied frequency shift. Our methods can be easily extended to multiple-element coupled oscillator networks.

Modeling spike-wave discharges by a complex network of neuronal oscillators

Purpose: The organization of neural networks and the mechanisms, which generate the highly stereotypical for absence epilepsy spike-wave discharges (SWDs) is heavily debated. Here we describe such a model which can both reproduce the characteristics of SWDs and dynamics of coupling between brain regions, relying mainly on properties of hierarchically organized networks of a large number of neuronal oscillators.Model: We used a two level mesoscale model. The first level consists of three structures: the nervus trigeminus serving as an input, the thalamus and the somatosensory cortex; the second level of a group of nearby situated neurons belonging to one of three modeled structures.Results: The model reproduces the main features of the transition from normal to epileptiformic activity and its spontaneous abortion: an increase in the oscillation amplitude, the emergence of the main frequency and its higher harmonics, and the ability to generate trains of seizures. The model was stable with respect to variations in the structure of couplings and to scaling. The analyzes of the interactions between model structures from their time series using Granger causality method showed that the model reproduced the preictal coupling increase detected previously from experimental data.Conclusion: SWDs can be generated by changes in network organization. It is proposed that a specific pathological architecture of couplings in the brain is necessary to allow the transition from normal to epileptiformic activity, next to by others modeled and reported factors referring to complex, intrinsic, and synaptic mechanisms.

A universal order parameter for synchrony in networks of limit cycle oscillators.

We analyze the properties of order parameters measuring synchronization and phase locking in complex oscillator networks. First, we review network order parameters previously introduced and reveal several shortcomings: none of the introduced order parameters capture all transitions from incoherence over phase locking to full synchrony for arbitrary, finite networks. We then introduce an alternative, universal order parameter that accurately tracks the degree of partial phase locking and synchronization, adapting the traditional definition to account for the network topology and its influence on the phase coherence of the oscillators. We rigorously prove that this order parameter is strictly monotonously increasing with the coupling strength in the phase locked state, directly reflecting the dynamic stability of the network. Furthermore, it indicates the onset of full phase locking by a diverging slope at the critical coupling strength. The order parameter may find applications across systems where different types of synchrony are possible, including biological networks and power grids.

Vertex coloring of graphs via phase dynamics of coupled oscillatory networks

While Boolean logic has been the backbone of digital information processing, there exist classes of computationally hard problems wherein this paradigm is fundamentally inefficient. Vertex coloring of graphs, belonging to the class of combinatorial optimization, represents one such problem. It is well studied for its applications in data sciences, life sciences, social sciences and technology, and hence, motivates alternate, more efficient non-Boolean pathways towards its solution. Here we demonstrate a coupled relaxation oscillator based dynamical system that exploits insulator-metal transition in Vanadium Dioxide (VO2) to efficiently solve vertex coloring of graphs. Pairwise coupled VO2 oscillator circuits have been analyzed before for basic computing operations, but using complex networks of VO2 oscillators, or any other oscillators, for more complex tasks have been challenging in theory as well as in experiments. The proposed VO2 oscillator network harnesses the natural analogue between optimization problems and energy minimization processes in highly parallel, interconnected dynamical systems to approximate optimal coloring of graphs. We further indicate a fundamental connection between spectral properties of linear dynamical systems and spectral algorithms for graph coloring. Our work not only elucidates a physics-based computing approach but also presents tantalizing opportunities for building customized analog co-processors for solving hard problems efficiently.

Plant shoots exhibit synchronized oscillatory motions

ABSTRACTIn animals, the ability to move has evolved as an important means of protection from predators and for enhancing nutrient uptake. In the animal kingdom, an individual's movements may become coordinated with those of other individuals that belong to the same group, which leads, for example, to the beautiful collective patterns that are observed in flocks of birds and schools of fish or in animal migration. Land plants, however, are fixed to the ground, which limits their movement and, apparently, their interactions and collective behaviors. We show that emergent maize plants grown in a group exhibit synchronized oscillatory motions that may be in-phase or anti-phase. These oscillations occur in short bursts and appear when the leaves rupture from the coleoptile tip. The appearance of these oscillations indicates an abrupt increase in the plant growth rate, which may be associated with a sudden change in the energy uptake for photosynthesis. Our results suggest that plant shoots behave as a complex network of biological oscillators, interacting through biophysical links, e.g. chemical substances or electric signals.

Growth, collapse, and self-organized criticality in complex networks.

Network growth is ubiquitous in nature (e.g., biological networks) and technological systems (e.g., modern infrastructures). To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands. We find that a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverges from the synchronous state in a cascading manner within a relatively short time period. In particular, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis reveals that the collapse size is approximately algebraically distributed, indicating the emergence of self-organized criticality. We demonstrate the generality of the phenomenon of synchronization collapse using a variety of complex network models, and uncover the underlying dynamical mechanism through an eigenvector analysis.

Constructing the suprachiasmatic nucleus: a watchmaker's perspective on the central clockworks.

The circadian system constrains an organism's palette of behaviors to portions of the solar day appropriate to its ecological niche. The central light-entrained clock in the suprachiasmatic nucleus (SCN) of the mammalian circadian system has evolved a complex network of interdependent signaling mechanisms linking multiple distinct oscillators to serve this crucial function. However, studies of the mechanisms controlling SCN development have greatly lagged behind our understanding of its physiological functions. We review advances in the understanding of adult SCN function, what has been described about SCN development to date, and the potential of both current and future studies of SCN development to yield important insights into master clock function, dysfunction, and evolution.

Synchronization in complex networks of phase oscillators: A survey

The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in real-world synchronization phenomena, that generalize the celebrated Kuramoto model, and that feature a rich phenomenology. We review the history and the countless applications of this model throughout science and engineering. We justify the importance of the widespread coupled oscillator model as a locally canonical model and describe some selected applications relevant to control scientists, including vehicle coordination, electric power networks, and clock synchronization. We introduce the reader to several synchronization notions and performance estimates. We propose analysis approaches to phase and frequency synchronization, phase balancing, pattern formation, and partial synchronization. We present the sharpest known results about synchronization in networks of homogeneous and heterogeneous oscillators, with complete or sparse interconnection topologies, and in finite-dimensional and infinite-dimensional settings. We conclude by summarizing the limitations of existing analysis methods and by highlighting some directions for future research.