2,241 research outputs found
Oscillations of complex networks
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
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
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
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
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
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
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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