2,452 research outputs found

    Oscillations of complex networks

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    A complex network processing information or physical flows is usually characterized by a number of macroscopic quantities such as the diameter and the betweenness centrality. An issue of significant theoretical and practical interest is how such a network responds to sudden changes caused by attacks or disturbances. By introducing a model to address this issue, we find that, for a finite-capacity network, perturbations can cause the network to \emph{oscillate} persistently in the sense that the characterizing quantities vary periodically or randomly with time. We provide a theoretical estimate of the critical capacity-parameter value for the onset of the network oscillation. The finding is expected to have broad implications as it suggests that complex networks may be structurally highly dynamic.Comment: 4 pages, 4 figures. submitte

    A robust relativistic quantum two-level system with edge-dependent currents and spin polarization

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    This work was supported by AFOSR under Grant No. FA9550-15-1-0151. LH was supported by NSFC under Grant No. 11422541.Peer reviewedPostprin

    The multiple effects of gradient coupling on network synchronization

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    Recent studies have shown that synchronizability of complex networks can be significantly improved by asymmetric couplings, and increase of coupling gradient is always in favor of network synchronization. Here we argue and demonstrate that, for typical complex networks, there usually exists an optimal coupling gradient under which the maximum network synchronizability is achieved. After this optimal value, increase of coupling gradient could deteriorate synchronization. We attribute the suppression of network synchronization at large gradient to the phenomenon of network breaking, and find that, in comparing with sparsely connected homogeneous networks, densely connected heterogeneous networks have the superiority of adopting large gradient. The findings are supported by indirect simulations of eigenvalue analysis and direct simulations of coupled nonidentical oscillator networks.Comment: 4 pages, 4 figure

    Transition to turbulence in Taylor-Couette ferrofluidic flow

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    Y.D. was supported by Basic Science Research Program of the Ministry of Education, Science and Technology under Grant No. NRF-2013R1A1A2010067. Y.C.L. was supported by AFOSR under Grant No. FA9550-12-1-0095.Peer reviewedPublisher PD

    Ring bursting behavior en route to turbulence in quasi two-dimensional Taylor-Couette flows

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    We investigate the quasi two-dimensional Taylor-Couette system in the regime where the radius ratio is close to unity - a transitional regime between three and two dimensions. By systematically increasing the Reynolds number we observe a number of standard transitions, such as one from the classical Taylor vortex flow (TVF) to wavy vortex flow (WVF), as well as the transition to fully developed turbulence. Prior to the onset of turbulence we observe intermittent burst patterns of localized turbulent patches, confirming the experimentally observed pattern of very short wavelength bursts (VSWBs). A striking finding is that, for Reynolds number larger than the onset of VSWBs, a new type of intermittently bursting behaviors emerge: burst patterns of azimuthally closed rings of various orders. We call them ring-burst patterns, which surround the cylinder completely but remain localized and separated by non-turbulent mostly wavy structures in the axial direction. We use a number of quantitative measures, including the cross-flow energy, to characterize the ring-burst patterns and to distinguish them from the background flow. The ring-burst patterns are interesting because it does not occur in either three- or two-dimensional Taylor-Couette flow: it occurs only in the transition, quasi two-dimensional regime of the system, a regime that is less studied but certainly deserves further attention so as to obtain deeper insights into turbulence

    Digital twins of nonlinear dynamical systems: A perspective

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    Digital twins have attracted a great deal of recent attention from a wide range of fields. A basic requirement for digital twins of nonlinear dynamical systems is the ability to generate the system evolution and predict potentially catastrophic emergent behaviors so as to providing early warnings. The digital twin can then be used for system "health" monitoring in real time and for predictive problem solving. In particular, if the digital twin forecasts a possible system collapse in the future due to parameter drifting as caused by environmental changes or perturbations, an optimal control strategy can be devised and executed as early intervention to prevent the collapse. Two approaches exist for constructing digital twins of nonlinear dynamical systems: sparse optimization and machine learning. The basics of these two approaches are described and their advantages and caveats are discussed.Comment: 12 pages, 3 figure

    Data based identification and prediction of nonlinear and complex dynamical systems

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    We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
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