Layered Chaos in Mean-field and Quantum Many-body Dynamics

We investigate the dimension of the phase space attractor of a quantum chaotic many-body ratchet in the mean-field limit. Specifically, we explore a driven Bose-Einstein condensate in three distinct dynamical regimes - Rabi oscillations, chaos, and self-trapping regime, and for each of them we calculate the correlation dimension. For the ground state of the ratchet formed by a system of field-free non-interacting particles, we find four distinct pockets of chaotic dynamics throughout these regimes. We show that a measurement of a local density in each of the dynamical regimes, has an attractor characterized with a higher fractal dimension, $D_{R}=2.59\pm0.01$, $D_{C}=3.93\pm0.04$, and $D_{S}=3.05\pm0.05$, as compared to the global measure of current, $D_{R}=2.07\pm0.02$, $D_{C}=2.96\pm0.05$, and $D_{S}=2.30\pm0.02$. We find that the many-body case converges to mean-field limit with strong sub-unity power laws in particle number $N$, namely $N^{\alpha}$ with $\alpha_{R}={0.28\pm0.01}$, $\alpha_{C}={0.34\pm0.067}$ and $\alpha_{S}={0.90\pm0.24}$ for each of the dynamical regimes mentioned above. The deviation between local and global measurement of the attractor's dimension corresponds to an increase towards high condensate depletion which remains constant for long time scales in both Rabi and chaotic regimes. The depletion is found to scale polynomially with particle number as $N^{\beta}$ with $\beta_{R}={0.51\pm0.004}$ and $\beta_{C}={0.18\pm0.004}$ for the two regimes. Thus, we find a strong deviation from the mean-field results, especially in the chaotic regime of the quantum ratchet. The ratchet also reveals quantum revivals in the Rabi and self-trapped regimes but not in the chaotic regime. Based on the obtained results we outline pathways for the identification and characterization of the emergent phenomena in driven many-body systems

Power-Law Sensitivity to Initial Conditions within a Logistic-like Family of Maps: Fractality and Nonextensivity

Power-law sensitivity to initial conditions, characterizing the behaviour of dynamical systems at their critical points (where the standard Liapunov exponent vanishes), is studied in connection with the family of nonlinear 1D logistic-like maps $x_{t+1} = 1 - a | x_t |^z, (z > 1; 0 < a \le 2; t=0,1,2,...)$ The main ingredient of our approach is the generalized deviation law \lim_{\Delta x(0) -> 0} \Delta x(t) / \Delta x(0)} = [1+(1-q)\lambda_q t]^{1/(1-q)} (equal to $e^{\lambda_1 t}$ for q=1, and proportional, for large t, to $t^{1/(1-q)}$ for $q \ne 1; q \in R$ is the entropic index appearing in the recently introduced nonextensive generalized statistics). The relation between the parameter q and the fractal dimension d_f of the onset-to-chaos attractor is revealed: q appears to monotonically decrease from 1 (Boltzmann-Gibbs, extensive, limit) to -infinity when d_f varies from 1 (nonfractal, ergodic-like, limit) to zero.Comment: LaTeX, 6 pages , 5 figure

Chaotic Time Series Analysis in Economics: Balance and Perspectives

To show that a mathematical model exhibits chaotic behaviour does not prove that chaos is also present in the corresponding data. To convincingly show that a system behaves chaotically, chaos has to be identified directly from the data. From an empirical point of view, it is difficult to distinguish between fluctuations provoked by random shocks and endogenous fluctuations determined by the nonlinear nature of the relation between economic aggregates. For this purpose, chaos tests test are developed to investigate the basic features of chaotic phenomena: nonlinearity, fractal attractor, and sensitivity to initial conditions. The aim of the paper is not to review the large body of work concerning nonlinear time series analysis in economics, about which much has been written, but rather to focus on the new techniques developed to detect chaotic behaviours in the data. More specifically, our attention will be devoted to reviewing the results reached by the application of these techniques to economic and financial time series and to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area.Economic dynamics, nonlinearity, tests for chaos, chaos

Real-time support for high performance aircraft operation

The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown

Dissipative Chaos in Semiconductor Superlattices

We consider the motion of ballistic electrons in a miniband of a semiconductor superlattice (SSL) under the influence of an external, time-periodic electric field. We use the semi-classical balance-equation approach which incorporates elastic and inelastic scattering (as dissipation) and the self-consistent field generated by the electron motion. The coupling of electrons in the miniband to the self-consistent field produces a cooperative nonlinear oscillatory mode which, when interacting with the oscillatory external field and the intrinsic Bloch-type oscillatory mode, can lead to complicated dynamics, including dissipative chaos. For a range of values of the dissipation parameters we determine the regions in the amplitude-frequency plane of the external field in which chaos can occur. Our results suggest that for terahertz external fields of the amplitudes achieved by present-day free electron lasers, chaos may be observable in SSLs. We clarify the nature of this novel nonlinear dynamics in the superlattice-external field system by exploring analogies to the Dicke model of an ensemble of two-level atoms coupled with a resonant cavity field and to Josephson junctions.Comment: 33 pages, 8 figure

Chaos Attractors as an Alignment Mechanism between Projects and Organizational Strategy

AbstractChaos attractors have been studied in detail in the biological and environmental sciences and used to explain phenomena such as the Butterfly effect. Limited research has been done to identify and understand the use of chaos attractors in projects to help with alignment of project activities towards the project objective throughout the entire project duration. This paper will explore the literature on the use of chaos attractors as alignment mechanism between projects and organizational strategy. A conceptual model and propositions are proposed that could form the basis for further research