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Ugur Tirnakli

Publications and source records attributed to Ugur Tirnakli.

At least 19 recordsLinked to original sources

Central Limit Behavior at the Edge of Chaos in the z-Logistic Map

We focus on the FeigenbaumCoulletTresser point of the dissipative one-dimensional z logistic map. We show that sums of iterates converge to q Gaussian distributions, which optimize the nonadditive entropic functional Sq under simple constraints. We derive a closedform prediction for the entropic index, and validate it numerically via data collapse for typical z values. The formula captures how the limiting law depends on the nonlinearity order and implies finite variance for z larger than 2 and divergent variance for z in between 1 and 2. These results extend edge of chaos central limit behavior beyond the standard case and provide a simple predictive law for unimodal maps with varying maximum order.

cond-mat.stat-mech↗

Independent Approximates provide a maximum likelihood estimate for heavy-tailed distributions

Heavy-tailed distributions are infamously difficult to estimate because their moments tend to infinity as the shape of the tail decay increases. Nevertheless, this study shows the utilization of a modified group of moments for estimating a heavy-tailed distribution. These modified moments are determined from powers of the original distribution. The nth-power distribution is guaranteed to have finite moments up to n-1. Samples from the nth-power distribution are drawn from n-tuple Independent Approximates, which are the set of independent samples grouped into n-tuples and sub-selected to be approximately equal to each other. We show that Independent Approximates are a maximum likelihood estimator for the generalized Pareto and the Student's t distributions, which are members of the family of coupled exponential distributions. We use the first (original), second, and third power distributions to estimate their zeroth (geometric mean), first, and second power-moments respectively. In turn, these power-moments are used to estimate the scale and shape of the distributions. A least absolute deviation criteria is used to select the optimal set of Independent Approximates. Estimates using higher powers and moments are possible though the number of n-tuples that are approximately equal may be limited.

stat.ME↗

Generalization of the Gauss Map: A jump into chaos with universal features

The Gauss map (or continued fraction map) is an important dissipative one-dimensional discrete-time dynamical system that exhibits chaotic behaviour and which generates a symbolic dynamics consisting of infinitely many different symbols. Here we introduce a generalization of the Gauss map which is given by $x_{t+1}=\frac{1}{x_t^α} - \Bigl[\frac{1}{x_t^α} \Bigr]$ where $α\geq 0$ is a parameter and $x_t \in [0,1]$ ($t=0,1,2,3,\ldots$). The symbol $[\dots ]$ denotes the integer part. This map reduces to the ordinary Gauss map for $α=1$. The system exhibits a sudden `jump into chaos' at the critical parameter value $α=α_c \equiv 0.241485141808811\dots$ which we analyse in detail in this paper. Several analytical and numerical results are established for this new map as a function of the parameter $α$. In particular, we show that, at the critical point, the invariant density approaches a $q$-Gaussian with $q=2$ (i.e., the Cauchy distribution), which becomes infinitely narrow as $α\to α_c^+$. Moreover, in the chaotic region for large values of the parameter $α$ we analytically derive approximate formulas for the invariant density, by solving the corresponding Perron-Frobenius equation. For $α\to \infty$ the uniform density is approached. We provide arguments that some features of this transition scenario are universal and are relevant for other, more general systems as well.

cond-mat.stat-mech↗

Fourier's law breakdown for the planar-rotor chain with long-range coupling

The thermal conductivity, $κ$, of a homogeneous chain of generically-ranged interacting planar rotors, more precisely the inertial $α-XY$ model, is numerically studied with the coupling constant decaying as $r^{-α}$. The conductance ($σ\equiv κ/L$) is calculated for typical values of $α$, temperature $T$ and lattice size $L$. For large $L$, the results can be collapsed into an universal $q$-stretched exponential form, namely $L^{δ_α} σ\propto e_{q_α}^{-B_α(L^{γ_α}T)^{η_α}}$, where $e_q^z\equiv [1+ (1-q)z]^{1/(1-q)}$. The parameters $(γ_α,δ_α,B_α, η_α)$ are $α$-dependent, and $q_α$ is the index of the $q$-stretched exponential. This form is achievable due to the ratio $η_α/(q-1)$ being almost constant with respect to the lattice size $L$. Two distinct regimes are identified: for $0 \le α< 2$, the Fourier law is violated, whereas for $α\ge 2$, it is satisfied. These findings provide significant insights into heat conduction mechanisms in systems with long-range interactions.

cond-mat.stat-mech↗

First-principle validation of Fourier's law in d=1,2,3 classical systems

We numerically study the thermal transport in the classical inertial nearest-neighbor XY ferromagnet in $d=1,2,3$, the total number of sites being given by $N=L^d$, where $L$ is the linear size of the system. For the thermal conductance $σ$, we obtain $σ(T,L)\, L^{δ(d)} = A(d)\, e_{q(d)}^{- B(d)\,[L^{γ(d)}T]^{η(d)}}$ (with $e_q^z \equiv [1+(1-q)z]^{1/(1-q)};\,e_1^z=e^z;\,A(d)>0;\,B(d)>0;\,q(d)>1;\,η(d)>2;\,δ\ge 0; \,γ(d)>0)$, for all values of $L^{γ(d)}T$ for $d=1,2,3$. In the $L\to\infty$ limit, we have $σ\propto 1/L^{ρ_σ(d)}$ with $ρ_σ(d)= δ(d)+ γ(d) η(d)/[q(d)-1]$. The material conductivity is given by $κ=σL^d \propto 1/L^{ρ_κ(d)}$ ($L\to\infty$) with $ρ_κ(d)=ρ_σ(d)-d$. Our numerical results are consistent with 'conspiratory' $d$-dependences of $(q,η,δ,γ)$, which comply with normal thermal conductivity (Fourier law) for all dimensions.

cond-mat.stat-mech↗

Entropic extensivity and large deviations in the presence of strong correlations

The standard Large Deviation Theory (LDT) mirrors the Boltzmann-Gibbs (BG) factor which describes the thermal equilibrium of short-range Hamiltonian systems, the velocity distribution of which is Maxwellian. It is generically applicable to systems satisfying the Central Limit Theorem (CLT), among others. When we focus instead on stationary states of typical complex systems (e.g., classical long-range Hamiltonian systems), both the CLT and LDT need to be generalized. We focus here on a scale-invariant stochastic process involving strongly-correlated exchangeable random variables which, through the Laplace-de Finetti theorem, is known to yield a long-tailed $Q$-Gaussian $N\to\infty$ attractor in the space of distributions ($1 < Q<3)$. We present strong numerical indications that the corresponding LDT probability distribution is given by $P(N,z)=P_0\,e_q^{-r_q(z)N}=P_0[1-(1-q)r_q(z)N]^{1/(1-q)}$ with $q=2-1/Q \in (1,5/3)$. The rate function $r_q(z)$ seemingly equals the relative nonadditive $q_r$-entropy per particle, with $q_r \simeq \frac{7}{10} + \frac{6}{10}\frac{1}{Q-1}$, thus exhibiting a singularity at $Q=1$ and recovering the BG value $q_r=1$ in the $Q \to 3$ limit. Let us emphasize that the extensivity of $r_q(z)N$ appears to be verified, consistently with what is expected, from the Legendre structure of thermodynamics, for a total entropy. The present analysis of a relatively simple model somewhat mirroring spin-1/2 long-range-interacting ferromagnets (e.g., with strongly anisotropic XY coupling) might be helpful for a deeper understanding of nonequilibrium systems with global correlations and other complex systems.

physics.gen-ph↗

Approaching a large deviation theory for complex systems

The standard Large Deviation Theory (LDT) is mathematically illustrated by the Boltzmann-Gibbs factor which describes the thermal equilibrium of short-range-interacting many-body Hamiltonian systems, the velocity distribution of which is Maxwellian. It is generically applicable to systems satisfying the Central Limit Theorem (CLT). When we focus instead on stationary states of typical complex systems (e.g., classical long-range-interacting many-body Hamiltonian systems, such as self-gravitating ones), the CLT, and possibly also the LDT, need to be generalised. Specifically, when the $N\to\infty$ attractor ($N$ being the number of degrees of freedom) in the space of distributions is a $Q$-Gaussian (a nonadditive $q$-entropy-based generalisation of the standard Gaussian case, which is recovered for $Q=1$) related to a $Q$-generalised CLT, we expect the LDT probability distribution to asymptotically approach a power law. Consistently with available strong numerical indications for probabilistic models, this behaviour possibly is that associated to a $q$-exponential (defined as $e_q^x\equiv\left[1+(1-q)x\right]^{1/(1-q)}$, which is the generalisation of the standard exponential form, straightforwardly recovered for $q=1$); $q$ and $Q$ are expected to be simply connected, including the particular case $q=Q=1$. The argument of such $q$-exponential would be expected to be proportional to $N$, analogously to the thermodynamical entropy of many-body Hamiltonian systems. We provide here numerical evidence supporting the asymptotic power-law by analysing the standard map, the coherent noise model for biological extinctions and earthquakes, the Ehrenfest dog-flea model, and the random-walk avalanches.

cond-mat.stat-mech↗

Dynamical robustness of discrete conservative systems: Harper and generalized standard maps

In recent years, statistical characterization of the discrete conservative dynamical systems (more precisely, paradigmatic examples of area-preserving maps such as the standard and the web maps) has been analyzed extensively and shown that, for larger parameter values for which the Lyapunov exponents are largely positive over the entire phase space, the probability distribution is a Gaussian, consistent with Boltzmann-Gibbs (BG) statistics. On the other hand, for smaller parameter values for which the Lyapunov exponents are virtually zero over the entire phase space, we verify this distribution appears to approach a $q$-Gaussian (with $q \simeq 1.935$), consistent with $q$-statistics. Interestingly, if the parameter values are in between these two extremes, then the probability distributions happen to exhibit a linear combination of these two behaviours. Here, we numerically show that the Harper map is also in the same universality class of the maps discussed so far. This constitutes one more evidence on the robustness of this behavior whenever the phase space consists of stable orbits. Then, we propose a generalization of the standard map for which the phase space includes many sticky regions, changing the previously observed simple linear combination behavior to a more complex combination.

cond-mat.stat-mech↗

Statistical characterization of discrete conservative systems: The web map

We numerically study the two-dimensional, area preserving, web map. When the map is governed by ergodic behavior, it is, as expected, correctly described by Boltzmann-Gibbs statistics, based on the additive entropic functional $S_{BG}[p(x)] = -k\int dx\,p(x) \ln p(x)$. In contrast, possible ergodicity breakdown and transitory sticky dynamical behavior drag the map into the realm of generalized $q$-statistics, based on the nonadditive entropic functional $S_q[p(x)]=k\frac{1-\int dx\,[p(x)]^q}{q-1}$ ($q \in {\cal R}; S_1=S_{BG}$). We statistically describe the system (probability distribution of the sum of successive iterates, sensitivity to the initial condition, and entropy production per unit time) for typical values of the parameter that controls the ergodicity of the map. For small (large) values of the external parameter $K$, we observe $q$-Gaussian distributions with $q=1.935\dots$ (Gaussian distributions), like for the standard map. In contrast, for intermediate values of $K$, we observe a different scenario, due to the fractal structure of the trajectories embedded in the chaotic sea. Long-standing non-Gaussian distributions are characterized in terms of the kurtosis and the box-counting dimension of chaotic sea.

cond-mat.stat-mech↗

Thermal conductance of the coupled-rotator chain: Influence of temperature and size

Thermal conductance of a homogeneous 1D nonlinear lattice system with neareast neighbor interactions has recently been computationally studied in detail by Li et al [Eur. Phys. J. B {\bf 88}, 182 (2015)], where its power-law dependence on temperature $T$ for high temperatures is shown. Here, we address its entire temperature dependence, in addition to its dependence on the size $N$ of the system. We obtain a neat data collapse for arbitrary temperatures and system sizes, and numerically show that the thermal conductance curve is quite satisfactorily described by a fat-tailed $q$-Gaussian dependence on $TN^{1/3}$ with $q \simeq 1.55$. Consequently, its $T \to\infty$ asymptotic behavior is given by $T^{-α}$ with $α=2/(q-1) \simeq 3.64$.

cond-mat.stat-mech↗

Statistical characterization of the standard map

The standard map, paradigmatic conservative system in the $(x,p)$ phase space, has been recently shown to exhibit interesting statistical behaviors directly related to the value of the standard map parameter $K$. A detailed numerical description is achieved in the present paper. More precisely, for large values of $K$, the Lyapunov exponents are neatly positive over virtually the entire phase space, and, consistently with Boltzmann-Gibbs (BG) statistics, we verify $q_{\text{ent}}=q_{\text{sen}}=q_{\text{stat}}=q_{\text{rel}}=1$, where $q_{\text{ent}}$ is the $q$-index for which the nonadditive entropy $S_q \equiv k \frac{1-\sum_{i=1}^W p_i^q}{q-1}$ (with $S_1=S_{BG} \equiv -k\sum_{i=1}^W p_i \ln p_i$) grows linearly with time before achieving its $W$-dependent saturation value; $q_{\text{sen}}$ characterizes the time increase of the sensitivity $ξ$ to the initial conditions, i.e., $ξ\sim e_{q_{\text{sen}}}^{λ_{q_{\text{sen}}} \,t}\;(λ_{q_{\text{sen}}}>0)$, where $e_q^z \equiv[1+(1-q)z]^{1/(1-q)}$; $q_{\text{stat}}$ is the index associated with the $q_{\text{stat}}$-Gaussian distribution of the time average of successive iterations of the $x$-coordinate; finally, $q_{\text{rel}}$ characterizes the $q_{\text{rel}}$-exponential relaxation with time of the entropy $S_{q_{\text{ent}}}$ towards its saturation value. In remarkable contrast, for small values of $K$, the Lyapunov exponents are virtually zero over the entire phase space, and, consistently with $q$-statistics, we verify $q_{\text{ent}}=q_{\text{sen}}=0$, $q_{\text{stat}} \simeq 1.935$, and $q_{\text{rel}} \simeq1.4$. The situation corresponding to intermediate values of $K$, where both stable orbits and a chaotic sea are present, is discussed as well. The present results transparently illustrate when BG or $q$-statistical behavior are observed.

cond-mat.stat-mech↗

On the relevance of q-distribution functions: The return time distribution of restricted random walker

There exist a large literature on the application of $q$-statistics to the out-of-equilibrium non-ergodic systems in which some degree of strong correlations exists. Here we study the distribution of first return times to zero, $P_R(0,t)$, of a random walk on the set of integers $\{0,1,2,...,L\}$ with a position dependent transition probability given by $|n/L|^a$. We find that for all values of $a\in[0,2]$ $P_R(0,t)$ can be fitted by $q$-exponentials, but only for $a=1$ is $P_R(0,t)$ given exactly by a $q$-exponential in the limit $L\rightarrow\infty$. This is a remarkable result since the exact analytical solution of the corresponding continuum model represents $P_R(0,t)$ as a sum of Bessel functions with a smooth dependence on $a$ from which we are unable to identify $a=1$ as of special significance. However, from the high precision numerical iteration of the discrete Master Equation, we do verify that only for $a=1$ is $P_R(0,t)$ exactly a $q$-exponential and that a tiny departure from this parameter value makes the distribution deviate from $q$-exponential. Further research is certainly required to identify the reason for this result and also the applicability of $q$-statistics and its domain.

math-ph↗

A closer look at coupled logistic maps at the edge of chaos

We focus on a linear chain of $N$ first-neighbor-coupled logistic maps at their edge of chaos in the presence of a common noise. This model, characterised by the coupling strength $ε$ and the noise width $σ_{max}$, was recently introduced by Pluchino et al [Phys. Rev. E {\bf 87}, 022910 (2013)]. They detected, for the time averaged returns with characteristic return time $τ$, possible connections with $q$-Gaussians, the distributions which optimise, under appropriate constraints, the nonadditive entropy $S_q$, basis of nonextensive statistics mechanics. We have here a closer look on this model, and numerically obtain probability distributions which exhibit a slight asymmetry for some parameter values, in variance with simple $q$-Gaussians. Nevertheless, along many decades, the fitting with $q$-Gaussians turns out to be numerically very satisfactory for wide regions of the parameter values, and we illustrate how the index $q$ evolves with $(N, τ, ε, σ_{max})$. It is nevertheless instructive on how careful one must be in such numerical analysis. The overall work shows that physical and/or biological systems that are correctly mimicked by the Pluchino et al model are thermostatistically related to nonextensive statistical mechanics when time-averaged relevant quantities are studied.

cond-mat.stat-mech↗

The standard map: From Boltzmann-Gibbs statistics to Tsallis statistics

As well known, Boltzmann-Gibbs statistics is the correct way of thermostatistically approaching ergodic systems. On the other hand, nontrivial ergodicity breakdown and strong correlations typically drag the system into out-of-equilibrium states where Boltzmann-Gibbs statistics fails. For a wide class of such systems, it has been shown in recent years that the correct approach is to use Tsallis statistics instead. Here we show how the dynamics of the paradigmatic conservative (area-preserving) standard map exhibits, in an exceptionally clear manner, the crossing from one statistics to the other. Our results unambiguously illustrate the domains of validity of both Boltzmann-Gibbs and Tsallis statistics.

cond-mat.stat-mech↗

Skewness and kurtosis analysis for non-Gaussian distributions

In a recent paper [\textit{M. Cristelli, A. Zaccaria and L. Pietronero, Phys. Rev. E 85, 066108 (2012)}], Cristelli \textit{et al.} analysed relation between skewness and kurtosis for complex dynamical systems and identified two power-law regimes of non-Gaussianity, one of which scales with an exponent of 2 and the other is with $4/3$. Finally the authors concluded that the observed relation is a universal fact in complex dynamical systems. Here, we test the proposed universal relation between skewness and kurtosis with large number of synthetic data and show that in fact it is not universal and originates only due to the small number of data points in the data sets considered. The proposed relation is tested using two different non-Gaussian distributions, namely $q$-Gaussian and Levy distributions. We clearly show that this relation disappears for sufficiently large data sets provided that the second moment of the distribution is finite. We find that, contrary to the claims of Cristelli \textit{et al.} regarding a power-law scaling regime, kurtosis saturates to a single value, which is of course different from the Gaussian case ($K=3$), as the number of data is increased. On the other hand, if the second moment of the distribution is infinite, then the kurtosis seems to never converge to a single value. The converged kurtosis value for the finite second moment distributions and the number of data points needed to reach this value depend on the deviation of the original distribution from the Gaussian case. We also argue that the use of kurtosis to compare distributions to decide which one deviates from the Gaussian more can lead to incorrect results even for finite second moment distributions for small data sets, whereas it is totally misleading for infinite second moment distributions where the difference depends on $N$ for all finite $N$.

cond-mat.stat-mech↗

Generalized Huberman-Rudnick scaling law and robustness of $q$-Gaussian probability distributions

We generalize Huberman-Rudnick universal scaling law for all periodic windows of the logistic map and show the robustness of $q$-Gaussian probability distributions in the vicinity of chaos threshold. Our scaling relation is universal for the self-similar windows of the map which exhibit period-doubling subharmonic bifurcations. Using this generalized scaling argument, for all periodic windows, as chaos threshold is approached, a developing convergence to $q$-Gaussian is numerically obtained both in the central regions and tails of the probability distributions of sums of iterates.

nlin.CD↗

Canonical equilibrium distribution derived from Helmholtz potential

Plastino and Curado [Phys. Rev. E 72, 047103 (2005)] recently determined the equilibrium probability distribution for the canonical ensemble using only phenomenological thermodynamical laws as an alternative to the entropy maximization procedure of Jaynes. In the current paper we present another alternative derivation of the canonical equilibrium probability distribution, which is based on the definition of the Helmholtz free energy (and its being constant at the equilibrium) and the assumption of the uniqueness of the equilibrium probability distribution. Noting that this particular derivation is applicable for all trace-form entropies, we also apply it to the Tsallis entropy showing that the Tsallis entropy yields genuine inverse power laws.

cond-mat.stat-mech↗

The Second Law For the Transitions Between the Non-equilibrium Steady States

We show that the system entropy change for the transitions between non-equilibrium steady states arbitrarily far from equilibrium for any constituting process is given by the relative entropy of the distributions of these steady states. This expression is then shown to relate to the dissipation relations of both Vaikuntanathan and Jarzynski [EPL 87, 60005 (2009)] and Kawai, Parrondo and Van den Broeck [Phys. Rev. Lett. 98, 080602 (2007)] in the case of energy-conserving driving.

cond-mat.stat-mech↗