Low-dimensional Chaos Research Articles

Chaos via type-II intermittency in a forced globally unstable jet

We explore the transition to chaos in a prototypical hydrodynamic oscillator, namely a globally unstable low-density jet subjected to external time-periodic forcing. As the forcing strengthens at an off-resonant frequency, we find that the jet exhibits a sequence of nonlinear states: period-1 limit cycle $\rightarrow $ quasiperiodicity $\rightarrow$ intermittency $\rightarrow$ low-dimensional chaos. We show that the intermittency obeys type-II Pomeau–Manneville dynamics by analysing the first return map and the scaling properties of the quasiperiodic lifetimes between successive chaotic epochs. By providing experimental evidence of the type-II intermittency route to chaos in a globally unstable jet, this study reinforces the idea that strange attractors emerge via universal mechanisms in open self-excited flows, facilitating the development of instability control strategies based on chaos theory.

Ground-induced suppression of chaos in the self-excited flow behind a plunging airfoil

We numerically investigate the forced synchronization of the self-excited flow behind a plunging airfoil in ground effect at a Reynolds number of Re = 1000. On varying the plunging amplitude and frequency, we find a rich array of nonlinear dynamics, such as a period-1 limit cycle due to natural vortex shedding as well as two-frequency quasiperiodicity on a torus attractor (T2). For certain non-resonant plunging frequencies without a ground surface, we find that low-dimensional chaos emerges via the Ruelle–Takens–Newhouse route. However, we find that the chaos can be suppressed by introducing a ground surface, inducing a direct transition from T2 quasiperiodicity to 1:1 phase locking as the plunging amplitude rises over the boundaries of the Arnold tongue. Apart from suppressing chaos, the ground surface also causes the lift and drag coefficients to become less sensitive to the plunging motion itself. Knowledge of the critical plunging conditions required for forced synchronization and chaos could be useful in various engineering applications, such as the design of pico air vehicles.

Measuring the Unpredictability of Disruptive Change: The Comparison of the Inkjet Printer and Digital Photography

Disruptive innovation has become a core topic for technology management and indeed a central aspect of the modern industrial revolution. Although considerable attention has been devoted to its emergent properties and selection outcomes, less focus has been given to understand how disruption emerges through evolving and complex interactions that occur during that process. To address this shortfall, this article draws on chaos theory to examine the nature of disruptive innovation as a nonlinear dynamic system. Disruptive innovation can be considered nonlinear because it is time-dependent and deviation-absorbing, and it revolves around identifiable types of attractors appearing in a deterministic context or trajectory. To illustrate and advance these ideas, we study inkjet printer and digital photography technologies using patent application data collected from the United States Patent and Trademark Office. We employ a nonlinear analysis of the correlation dimension to measure the unpredictability of disruptive change and found that both chosen technologies evolved through a nonlinear dynamic process. Furthermore, in light of chaos theory and by way of comparison, we indicated two types of disruption: high-dimensional and low-dimensional chaos. Implications for innovation theory and business practice are explored herein.

The reconstruction of flows from spatiotemporal data by autoencoders

Artificial neural networks have become essential tools in data science for uncovering insights from complex data. However, they are usually seen as black boxes. In this work we explore how an autoencoder processes complex spatiotemporal information. We analyze the topological structure of reconstructed flows in the latent space of an autoencoder for two distinct test cases. The first case involves a synthetic spatiotemporal pattern for the temperature field in a convective problem, illustrating a classic extended system that exhibits low-dimensional chaos. The second case focuses on an experimental recording of the labial oscillations responsible for sound production in an avian vocal organ, as an example of periodic dynamics in a biological system. We find that the state representation in its latent space can be topologically equivalent to the phase space of the problem. Autoencoders thus retain phase space representations of the data hidden in its latent layer.

Benchmarking sparse system identification with low-dimensional chaos

Sparse system identification is the data-driven process of obtaining parsimonious differential equations that describe the evolution of a dynamical system, balancing model complexity and accuracy. There has been rapid innovation in system identification across scientific domains, but there remains a gap in the literature for large-scale methodological comparisons that are evaluated on a variety of dynamical systems. In this work, we systematically benchmark sparse regression variants by utilizing the dysts standardized database of chaotic systems introduced by Gilpin (in: Advances in neural information processing systems (NeurIPS), 2021. arXiv:2110.05266 ). In particular, we demonstrate how this open-source tool can be used to quantitatively compare different methods of system identification. To illustrate how this benchmark can be utilized, we perform a large comparison of four algorithms for solving the sparse identification of nonlinear dynamics (SINDy) optimization problem, finding strong performance of the original algorithm and a recent mixed-integer discrete algorithm. In all cases, we used ensembling to improve the noise robustness of SINDy and provide statistical comparisons. In addition, we show very compelling evidence that the weak SINDy formulation provides significant improvements over the traditional method, even on clean data. Lastly, we investigate how Pareto-optimal models generated from SINDy algorithms depend on the properties of the equations, finding that the performance shows no significant dependence on a set of dynamical properties that quantify the amount of chaos, scale separation, degree of nonlinearity, and the syntactic complexity.

On the Existence of Low-Dimensional Chaos of the Tropical Cyclone Intensity in an Idealized Axisymmetric Simulation

Abstract This study examines the potential limit in predicting tropical cyclone (TC) intensity under idealized conditions. Using the phase-space reconstruction method for TC intensity time series obtained from the CM1 idealized simulations, it is found that CM1 axisymmetric dynamics contain low-dimensional chaos at the maximum intensity equilibrium. Examination of several attractor invariants including the largest Lyapunov exponent, the Sugihara–May correlation, and the correlation dimension captures a consistent range of the chaotic attractor dimension between 4 and 5 for TC intensity at the maximum intensity equilibrium. In addition, the intensity error doubling time estimated from the largest Lyapunov exponent is roughly 1–3 h, which accords with the decay time obtained from the Sugihara–May correlation. Furthermore, the findings in this study reveal a relatively short TC intensity predictability limit for CM1, which is ∼3–9 h based on the maximum tangential wind but noticeably longer for the minimum central pressure (∼12–18 h) after reaching the mature stage. So long as the traditional metrics for TC intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that TC intensity forecast errors will not be reduced indefinitely in any model, even in the absence of all model and observational errors. As such, the future improvement of TC intensity forecast should be based on different metrics beyond the absolute intensity errors that are currently used in real-time intensity verification. Significance Statement Using the phase-space reconstruction method for tropical cyclone (TC) intensity time series obtained from idealized axisymmetric simulations, we show that TC axisymmetric dynamics in CM1 possesses low-dimensional chaos at the maximum intensity equilibrium. This low-dimensional dynamics explains the long tradition of representing TC intensity by a few measures as in the current practice. The chaotic property of CM1 axisymmetric dynamics also suggests a relatively short predictability range for TC intensity at the maximum intensity equilibrium. The potential existence of low-dimensional chaos for TC intensity in CM1 idealized simulations as found in this study supports the use of different intensity verification metrics beyond the traditional absolute intensity errors currently used in operational model evaluation.

Pinning in a system of swarmalators.

We study a population of swarmalators (swarming/mobile oscillators) which run on a ring and are subject to random pinning. The pinning represents the tendency of particles to stick to defects in the underlying medium which competes with the tendency to sync and swarm. The result is rich collective behavior. A highlight is low dimensional chaos which in systems of ordinary, Kuramoto-type oscillators is uncommon. Some of the states (the phase wave and split phase wave) resemble those seen in systems of Janus matchsticks or Japanese tree frogs. The others (such as the sync and unsteady states) may be observable in systems of vinegar eels, electrorotated Quincke rollers, or other swarmalators moving in disordered environments.

Behaviour and onset of low-dimensional chaos with a periodically varying loss in single-mode homogeneously broadened laser

Abstract In this work, we numerically study the behaviour of the single-mode homogeneously broadened laser versus a large perturbation of the steady state. Periodic behaviour develops for suitable values of the ratio of the population decay rates ℘ \wp , which has been analytically predicted by the analytical approach developed in the previous works, and for a pumping parameter 2 C 2C situated below and above the instability threshold 2 C 2 th 2{C}_{2{\rm{th}}} . We here show that the adiabatic elimination of the polarisation with sinusoidal time-dependent perturbation of the cavity rate leads the single-mode homogenously broadened laser to exhibit chaotic emission, if the frequency ω \omega is equal to the natural frequency Δ P {\Delta }_{P} . Increasing the pumping parameter 2 C 2C , the laser undergoes a period-doubling sequence. This cascade of period doubling drives the laser towards chaos. We also propose a reformulation of our analytical procedure, which describes the self-pulsing regime of the single-mode homogeneously broadened laser. We here report that asymmetric aspect that appears in the time evolution of the electric field is due to phase effects between the electric field components.

Chaos arising from the hydrological behaviour of a floodplain river during the last century

AbstractThe hydrological regime is the main factor governing the functioning of floodplain rivers. A full comprehension of its dynamic leads to a better understanding of the system's behaviour. We analysed the daily water level of the Paraná River during the last century at three gauge stations using linear and non‐linear tools to characterise the hydrological dynamic and to analyse to what extent chaotic behaviour prevails. The three water level time series were characterised as non‐linear and non‐stationary by power spectrum, autocorrelation function, and surrogate test analyses. A strange attractor was developed when the phase space was reconstructed, having a low dimensional chaos supported by correlation dimension, positive maximum Lyapunov exponents, and recurrence quantification analysis. The sample entropy analysis shows that the system resulted unpredictable beyond a temporal threshold, and with an intermediate hydrological complexity, while Hurst exponent characterised it as persistent and with sensitive dependence on initial conditions. In a general overview, all the evidence obtained indicates that the Paraná River's behaviour is at the edge of chaos. A latitudinal gradient of decreasing chaoticity was observed as the floodplain extent increased, whereas complexity was highest at the intermediate river station due to the inflow of tributaries with different hydrology. This paper attempts to offer some additional insights for understanding the hydrological behaviour of floodplain rivers and the most appropriate methods to understand their complexity.

Identification of Chaos in Financial Time Series to Forecast Nonperforming Loan

This paper discusses the importance of modeling financial time series as a chaotic dynamic rather than a stochastic system. The dynamical properties of a financial time series of an economic institution in Iran were analyzed to identify the potential occurrence of the low-dimensional deterministic chaos. This paper applies several classic nonlinear techniques for detecting the chaotic nature of the time series of loan payment portion and proposes a modified nonlinear predictor scheme for forecasting the future levels of the nonperforming loan. The auto mutual information was implemented to estimate the delay time dimension, and Cao’s approach, along with correlation dimension methodology, quantified the embedding dimension of the time series. The results reveal a low embedding dimension implying the chaotic nature exists in the financial data. The maximum Lyapunov exponent measure is also adopted to investigate the divergence or convergence of the trajectories. Since positive Lyapunov exponents are revealed, the long-term unpredictability of the time series is proved. Lastly, a modified nonlinear local approximator is developed to forecast the short-term history of the time series. Numerical simulations are provided to illustrate the adopted nonlinear techniques. The results reported in this paper could have implications for commercial bank managers who could use the nonlinear models for early detection of the possible nonperforming loans before they become uncontrollable.

The Deterministic Nature of Sensor-Based Information for Condition Monitoring of the Cutting Process

Condition monitoring of the cutting process is a core function of autonomous machining and its success strongly relies on sensed data. Despite the enormous amount of research conducted so far into condition monitoring of the cutting process, there are still limitations given the complexity underlining tool wear; hence, a clearer understanding of sensed data and its dynamical behavior is fundamental to sustain the development of more robust condition monitoring systems. The dependence of these systems on acquired data is critical and determines the success of such systems. In this study, data is acquired from an experimental setup using some of the commonly used sensors for condition monitoring, reproducing realistic cutting operations, and then analyzed upon their deterministic nature using different techniques, such as the Lyapunov exponent, mutual information, attractor dimension, and recurrence plots. The overall results demonstrate the existence of low dimensional chaos in both new and worn tools, defining a deterministic nature of cutting dynamics and, hence, broadening the available approaches to tool wear monitoring based on the theory of chaos. In addition, recurrence plots depict a clear relationship to tool condition and may be quantified considering a two-dimensional structural measure, such as the semivariance. This exploratory study unveils the potential of non-linear dynamics indicators in validating information strength potentiating other uses and applications.

A Chaos Analysis of the Dry Bulk Shipping Market

Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.

Low-dimensional chaos in the single wave model for self-consistent wave-particle Hamiltonian.

We analyze nonlinear aspects of the self-consistent wave-particle interaction using Hamiltonian dynamics in the single wave model, where the wave is modified due to the particle dynamics. This interaction plays an important role in the emergence of plasma instabilities and turbulence. The simplest case, where one particle (N=1) is coupled with one wave (M=1), is completely integrable, and the nonlinear effects reduce to the wave potential pulsating while the particle either remains trapped or circulates forever. On increasing the number of particles ( N=2, M=1), integrability is lost and chaos develops. Our analyses identify the two standard ways for chaos to appear and grow (the homoclinic tangle born from a separatrix, and the resonance overlap near an elliptic fixed point). Moreover, a strong form of chaos occurs when the energy is high enough for the wave amplitude to vanish occasionally.

Spatial and temporal Taylor’s law in 1D chaotic maps

By using low-dimensional chaotic maps, the power-law relationship established between the sample mean and variance called Taylor's Law (TL) is studied. In particular, we aim to clarify the relationship between TL from the spatial ensemble (STL) and the temporal ensemble (TTL). Since the spatial ensemble corresponds to independent sampling from a stationary distribution, we confirm that STL is explained by the skewness of the distribution. The difference between TTL and STL is shown to be originated in the temporal correlation of a dynamics. In case of logistic and tent maps, the quadratic relationship in the sample mean and variance, called Bartlett's law, is found analytically. On the other hand, TTL in the Hassell model can be well explained by the chunk structure of the trajectory, whereas the TTL of the Ricker model has a different mechanism originated from the specific form of the map.

Searching for signatures of chaos in γ-ray light curves of selected Fermi-LAT blazars

ABSTRACT Blazar variability appears to be stochastic in nature. However, a possibility of low-dimensional chaos was considered in the past, but with no unambiguous detection so far. If present, it would constrain the emission mechanism by suggesting an underlying dynamical system. We rigorously searched for signatures of chaos in Fermi-Large Area Telescope light curves of 11 blazars. The data were comprehensively investigated using the methods of nonlinear time-series analysis: phase-space reconstruction, fractal dimension, and maximal Lyapunov exponent (mLE). We tested several possible parameters affecting the outcomes, in particular the mLE, in order to verify the spuriousness of the outcomes. We found no signs of chaos in any of the analysed blazars. Blazar variability is either truly stochastic in nature or governed by high-dimensional chaos that can often resemble randomness.

Testing for Non-Linear Structures in Artificial and Real-World Financial Data with Recurrence Quantification Analysis

Early applications of empirical methods from chaos theory suggested the existence of low dimensional chaotic motion in empirical financial data. However, such results were questioned, and it is then believed that the search for low dimensional chaos in financial data was not successful. On the other hand, at the same time that the hypotheses that raw returns are independent and identically distributed (IID) is often rejected, they indeed present a quite small degree of autocorrelation. These facts suggest that prices in financial markets do not behave completely at random, although their hidden structures seem more complex than those observed in low dimensional chaotic systems. Previous work tested for non-linearity or the presence of low dimensional chaos in artificial financial data generated from the Lux-Marchesi model by means of the BDS and Kaplan tests. Addressing the same model, researchers extended those results by applying Hinich’s bi-spectral and White’s tests and introducing the application of Recurrence Quantification Analysis (RQA) on artificial financial data based on Recurrence Rate, Determinism, Entropy, and Maximal Diagonal Length. Contributing to this research, the present paper has two main goals: (i) to contrast previous findings with an RQA application on data generated by a more evolved of microscopic model of financial markets – the Structural Stochastic Volatility (SSV ) model; and (ii) to extend the RQA investigation above with additional recurrence measures (namely, Divergence, Laminarity, and Maximal Vertical Length) being applied to distinct real-world financial data. The objective is to assess if RQA results could help to distinguish between artificial and real-world data, even if linearity is rejected in both cases. It is shown evidence, in agreement previous findings, to support the rejection of linearity or low dimensional chaotic motion in an artificial financial time series generated from the SSV microscopic model. In addition, it is also shown that that RQA measures can help to discriminate artificial from real-world financial data, at least when specific RQA measures are considered.

2D mixed pseudo-random coupling PS map lattice and its application in S-box generation

In this paper, firstly, we investigate a new 1D PWLCM-Sin (PS) map which derived from PWLCM and Sin map by modulo operation. Due to the stronger parameter space, bigger Lyapunov exponents and better ergodicity than simple 1D map, the PS map is more suitable for local map of spatiotemporal dynamics. Secondly, with the novel 2D pseudo-random mixed coupling method we present a spatiotemporal chaos which used PS map as local map f(x). This spatiotemporal chaos named 2D Mixed pseudo-random Coupling PS Map Lattice (2DMCPML). The experimental results of bifurcation diagrams, Kolmogorov–Sinai entropy density and spatiotemporal chaotic diagrams showed that 2DMCPML has advantages of larger parameter space, more complex chaotic behavior and more ergodic output sequence than CML. Therefore, 2DMCPML is more suitable in cryptography than CML. Subsequently, we proposed a chaos-based random S-box design algorithm employed the spatial chaotic character of 2DMCPML to generate a large number of S-boxes. The cryptographic performance indicated that generated S-boxes can resist cryptanalysis attack well. Finally, four criteria bounds are set. The numbers of S-boxes satisfying these bounds generated by 2DMCPML and several 1D chaotic maps is calculated, respectively. The result showed that spatiotemporal chaos can generate more S-boxes with high cryptographic quality than low-dimensional chaos. This new discovery is significant to the development of some cryptographic researches such as dynamic S-box algorithm.

Laminar chaos in experiments and nonlinear delayed Langevin equations: A time series analysis toolbox for the detection of laminar chaos.

Recently, it was shown that certain systems with large time-varying delay exhibit different types of chaos, which are related to two types of time-varying delay: conservative and dissipative delays. The known high-dimensional turbulent chaos is characterized by strong fluctuations. In contrast, the recently discovered low-dimensional laminar chaos is characterized by nearly constant laminar phases with periodic durations and a chaotic variation of the intensity from phase to phase. In this paper we extend our results from our preceding publication [Hart, Roy, Müller-Bender, Otto, and Radons, Phys. Rev. Lett. 123, 154101 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.154101], where it is demonstrated that laminar chaos is a robust phenomenon, which can be observed in experimental systems. We provide a time series analysis toolbox for the detection of robust features of laminar chaos. We benchmark our toolbox by experimental time series and time series of a model system which is described by a nonlinear Langevin equation with time-varying delay. The benchmark is done for different noise strengths for both the experimental system and the model system, where laminar chaos can be detected, even if it is hard to distinguish from turbulent chaos by a visual analysis of the trajectory.

An Image Encryption Algorithm Based on Logistic-Fibonacci Cascade Chaos and 3D Bit Scrambling

Aiming at the problem that the existing bit scrambling encryption algorithm is not sensitive to the bit scrambling between 8 bits of one pixel, and the anti-noise and anti-selective plaintext attack ability is weak, a 3D cyclic shift bit scrambling image encryption algorithm is proposed in this paper. Discarding the scrambling mode of 8 bits of one pixel and the bits of all pixels, the pixel values are converted into binary arrays and then converted into 3D matrices in this paper. And the higher bit-planes and the lower bit-planes, which contain the plaintext information of different weights, are scrambled by cyclic shifting respectively. Therefore the sensitivity of the bit scrambling, the anti-noise attack ability of the algorithm and the randomness of intermediate ciphertext are improved. Moreover the randomness and the anti-noise ability can be adjusted by changing the number of higher bit-planes according to different encryption requirements. A new type of Logistic-Fibonacci (L-F) cascade chaos is constructed to generate random sequences, which solves the problem of blank windows in the uneven distribution of Logistic chaos. The initial value and the control parameters are increased, the sequence randomness is improved, and the fastness of low-dimensional chaos is preserved. By strongly correlating the key with SHA-256 of the plaintext, the key stream can change adaptively with the plaintext, which greatly improves the sensitivity of the plaintext and the ability of resisting the selective plaintext and ciphertext attack. The experiments show that the algorithm can encrypt all kinds of images with high efficiency, and can resist common attacks. It is a secure and reliable image encryption algorithm.

Generalized TCP-RED dynamical model for Internet congestion control

Abstract Adaptive management of traffic congestion in the Internet is a complex problem that can gain useful insights from a dynamical approach. In this paper we propose and analyze a one-dimensional, discrete-time nonlinear model for Internet congestion control at the routers. Specifically, the states correspond to the average queue sizes of the incoming data packets and the dynamical core consists of a monotone or unimodal mapping with a unique fixed point. This model generalizes a previous one in that additional control parameters are introduced via the data packet drop probability with the objective of enhancing stability. To make the analysis more challenging, the original model was shown to exhibit the usual features of low-dimensional chaos with respect to several system and control parameters, e.g., positive Lyapunov exponents and Feigenbaum-like bifurcation diagrams. We concentrate first on the theoretical aspects that may promote the unique stationary state of the system to a global attractor, which in our case amounts to global stability. In a second step, those theoretical results are translated into stability domains for robust setting of the new control parameters in practical applications. Numerical simulations confirm that the new parameters make it possible to extend the stability domains, in comparison with previous results. Therefore, the present work may lead to an adaptive congestion control algorithm with a more stable performance than other algorithms currently in use.