ABSTRACT
In this paper we report and analyze complex spatiotemporal dynamics recorded in electroconvection in the nematic liquid crystal I52, driven by an ac voltage slightly above the onset value. The instability mechanism creating the pattern is an oscillatory (Hopf) instability, giving rise to two pairs of counterpropagating rolls traveling in oblique directions relative to the unperturbed director axis. If a system of nonlinear partial differential equations shows the same set of unstable modes, the pattern above the onset is represented in a weakly nonlinear analysis as a superposition of the traveling rolls in terms of wave envelopes varying slowly in space and time. Motivated by this procedure, we extract slowly varying envelopes from the space-time data of the pattern, using a four-wave demodulation based on Fourier analysis. In order to characterize the spatiotemporal dynamics, we apply a variety of diagnostic methods to the envelopes, including the calculation of mean intensities and correlation lengths, global and local Karhunen-Loève decompositions in Fourier space and physical space, the location of holes, the identification of coherent vertical structures, and estimates of Lyapunov exponents. The results of this analysis provide strong evidence that our pattern exhibits extensive spatiotemporal chaos. One of its main characteristics is the presence of coherent structures of low and high intensities extended in the vertical (parallel to the director) direction.
ABSTRACT
In this paper the control of a periodically kicked mechanical rotor without gravity in the presence of noise is investigated. In recent work it was demonstrated that this system possesses many competing attracting states and thus shows the characteristics of a complex multistable system. We demonstrate that it is possible to stabilize the system at a desired attracting state even in the presence of high noise level. The control method is based on a recently developed algorithm [S. Gadaleta and G. Dangelmayr, Chaos 9, 775 (1999)] for the control of chaotic systems and applies reinforcement learning to find a global optimal control policy directing the system from any initial state towards the desired state in a minimum number of iterations. Being data-based, the method does not require any information about governing dynamical equations.
ABSTRACT
A system of coupled bistable Hopf oscillators with an external periodic input source was used to model the ability of interacting neural populations to synchronize and desynchronize in response to variations of the input signal. We propose that, in biological systems, the settings of internal and external coupling strengths will affect the behaviour of the system to a greater degree than the input frequency. While input frequency and coupling strength were varied, the spatio-temporal dynamics of the network was examined by the bi-orthogonal decomposition technique. Within this method, effects of variation of input frequency and coupling strength were analyzed in terms of global, spatial and temporal mode entropy and energy, using the spatio-temporal data of the system. We observed a discontinuous evolution of spatio-temporal patterns depending sensitively on both the input frequency and the internal and external coupling strengths of the network.
