Scattering from a classically chaotic repellor

According to the van der Waals picture, attractive and repulsive forces play distinct roles in the structure of simple fluids. Here, we examine their roles in dynamics; specifically, in the degree of deterministic chaos using the Kolmogorov-Sinai (KS) entropy rate and the spectra of Lyapunov exponents. With computer simulations of three-dimensional Lennard-Jones and Weeks-Chandler-Andersen fluids, we find repulsive forces dictate these dynamical properties, with attractive forces reducing the KS entropy at a given thermodynamic state. Regardless of interparticle forces, the maximal Lyapunov exponent is intensive for systems ranging from 200 to 2000 particles. Our finite-size scaling analysis also shows that the KS entropy is both extensive (a linear function of system-size) and additive. Both temperature and density control the "dynamical chemical potential," the rate of linear growth of the KS entropy with system size. At fixed system-size, both the KS entropy and the largest exponent exhibit a maximum as a function of density. We attribute the maxima to the competition between two effects: as particles are forced to be in closer proximity, there is an enhancement from the sharp curvature of the repulsive potential and a suppression from the diminishing free volume and particle mobility. The extensivity and additivity of the KS entropy and the intensivity of the largest Lyapunov exponent, however, hold over a range of temperatures and densities across the liquid and liquid-vapor coexistence regimes.

This thesis is dedicated to studying the theory of entropy and its relation to the Kolmogorov complexity. Originating in physics, the notion of entropy was introduced to mathematics by C. E. Shannon as a way of measuring the rate at which information is coming from a data source. There are, however, a few different ways of telling how much information there is: an alternative approach to quantifying the amount of information is the Kolmogorov complexity, which was proposed by A. N. Kolmogorov. The Shannon entropy is the key ingredient in the definition of the Kolmogorov-Sinai entropy of a measure-preserving systems. Roughly speaking, the Kolmogorov-Sinai entropy is the expected amount of information in `Shannon sense' that one obtains per unit of time by observing the evolution of a measure-preserving system. In topological dynamics, the topological entropy takes place of the Kolmogorov-Sinai entropy. For metrizable systems, the topological entropy measures the exponential growth rate of the number of distinguishable partial orbits of length n as n tends to infinity. Originally defined for Z-actions, the 'classical' theories of entropy were later extended to actions of amenable groups. We provide a necessary background on amenable groups, topological/measurepreserving dynamics and the entropy theory in Chapters 1, 2, 3 and 5. The main focus of this thesis is extending the following results. First of all, a common generalization of the topological and the Kolmogorov-Sinai entropy theories for Z-systems was suggested by G. Palm. We provide an abstract generalization of the work of Palm for actions of discrete amenable groups in the language of measurement functors in Chapter 6. Secondly, we investigate the connection of entropy and Kolmogorov complexity. Originally, the equality between the topological entropy and a certain quantity measuring maximal asymptotic Kolmogorov complexity of the trajectories was established by A. A. Brudno for subshifts over Z. Later, he proved the equality of the Kolmogorov-Sinai entropy and the asymptotic Kolmogorov complexity of almost every trajectory for ergodic subshifts over Z. We provide a generalization of these results as follows. Firstly, in Chapter 4 we give a background on computability and Kolmogorov complexity and, further, introduce computable Folner monotilings, which are central in our extensions of Brudno's results. We treat the 'first' and the 'second' theorems of Brudno in Chapter 7. The first theorem is generalized for subshifts over computable groups admitting computable Folner monotilings, while the second theorem is proved under the assertion of regularity of the monotiling, which we introduce in Chapter 7 as well.

The problem of simultaneously approximating a vector of irrational numbers with rationals is analyzed in a geometrical setting using notions of dynamical systems theory. We discuss here a (vectorial) multidimensional continued-fraction algorithm (MCFA) of additive type, the generalized mediant algorithm (GMA), and give a geometrical interpretation to it. We calculate the invariant measure of the GMA shift as well as its Kolmogorov-Sinai (KS) entropy for arbitrary number of irrationals. The KS entropy is related to the growth rate of denominators of the Euclidean algorithm. This is the first analytical calculation of the growth rate of denominators for any MCFA.

Now we want to discuss a number of topics that are essential for an understanding of dynamical systems theory and which also play a role in a more detailed discussion of the relation between transport theory and dynamical systems theory. We begin with a discussion of the Kolmogorov-Sinai (KS) entropy, which is a characteristic property of those deterministic dynamical systems with ‘randomness’ properties similar to Bernoulli shifts discussed earlier. The KS entropy is essential for formulating the escape-rate expressions for transport coefficients, to be discussed in Chapters 11 and 12.

Stable and metastable states and the formation and destruction of breathers in the discrete nonlinear Schrödinger equation

Two-dimensional horizontally periodic Rayleigh–Bénard convection between stress-free boundaries displays two distinct types of states, depending on the initial conditions. Roll states are composed of pairs of counter-rotating convection rolls. Windy states are dominated by strong horizontal wind (also called zonal flow) that is vertically sheared, precludes convection rolls and suppresses heat transport. Windy states occur only when the Rayleigh number $Ra$ is sufficiently above the onset of convection. At intermediate $Ra$ values, windy states can be induced by suitable initial conditions, but they undergo a transition to roll states after finite lifetimes. At larger $Ra$ values, where windy states have been observed for the full duration of simulations, it is unknown whether they represent chaotic attractors or only metastable states that would eventually undergo a transition to roll states. We study this question using direct numerical simulations of a fluid with a Prandtl number of 10 in a layer whose horizontal period is eight times its height. At each of seven $Ra$ values between $9\times 10^6$ and $2.25\times 10^7$ we have carried out 200 or more simulations, all from initial conditions leading to windy convection with finite lifetimes. The lifetime statistics at each $Ra$ indicate a memoryless process with survival probability decreasing exponentially in time. The mean lifetimes grow with $Ra$ approximately as $Ra^4$ . This analysis provides no $Ra$ value at which windy convection becomes stable; it might remain metastable at larger $Ra$ with extremely long lifetimes.

In this work we continue the study of historically the first version of dynamical entropy. This version was suggested in master’s thesis by D. Arov and went practically unnoticed. The main result of the paper is that the Arov entropy, like the Kolmogorov–Sinai entropy, has the affine property. This, in particular, allows constructing a variety of dynamical systems where the Arov entropy is not determined by the Kolmogorov–Sinai entropy.

We introduce the Mass Migration Process (MMP), a conservative particle system on ${\mathbb N}^{{\mathbb Z}^d}$. It consists in jumps of $k$ particles ($k\ge 1$) between sites, with a jump rate depending only on the state of the system at the departure and arrival sites of the jump. It generalizes misanthropes processes, hence in particular zero range and target processes. After the construction of MMP, our main focus is on its invariant measures. We obtain necessary and sufficient conditions for the existence of translation-invariant and invariant product probability measures. In the particular cases of asymmetric mass migration zero range and mass migration target dynamics, these conditions yield explicit solutions. If these processes are moreover attractive, we obtain a full characterization of all translation-invariant, invariant probability measures. We also consider attractiveness properties (through couplings) and condensation phenomena for MMP. We illustrate our results on many examples; in particular, we prove the coexistence of both condensation and attractiveness in one of them.

The eikonal approximation (instanton technique) is applied to the problem of large fluctuations of the number of species in spatially homogeneous chemical reactions with the probability density distribution described by a master equation. For both autocatalytic and nonautocatalytic reactions, the analysis of the distribution about a stable stationary state and of the transitions between coexisting stable states comes, to logarithmic accuracy, to the analysis of Hamiltonian dynamics of an auxiliary dynamical system. The latter can be done explicitly in a few cases, including one-species systems, systems with detailed balance, and systems close to the bifurcation points where the number of the stable states changes. In the last case, the fluctuations display universal features, and, for saddle-node bifurcation points, the logarithm of the probability of escape from the metastable state (per unit time) is proportional to the distance to the bifurcation point (in the parameter space) raised to the power 3/2. We compare the eikonal approximation for the stationary distribution of a master equation to Monte Carlo numerical solutions for two chemical two-variable systems with multiple stationary states, where none of the cited restrictions exists. For one of the systems in the pattern of optimal paths we observe caustics emanating from the saddle point.

We consider the Kolmogorov–Sinai entropy for dilute gases ofN hard disks or spheres. This can be expanded in density as , with a the diameterof the sphere or disk, n the density, and d the dimensionality of the system. We estimate the constantB by solving a linear differential equation for the approximate distribution of eigenvaluesof the inverse radius of curvature tensor. We compare the resulting values ofB both to previous estimates and to existing simulation results, finding very good agreementwith the latter. Also, we compare the distribution of eigenvalues of the inverse radius ofcurvature tensor resulting from our calculations to new simulation results. For most of thespectrum the agreement between our calculations and the simulations again is verygood.

We compute the Hausdorff dimension for an open class of iterated function schemes in in the C2 topology, in terms of the pressure function. This class is characterized by a domination condition that is not too strong, plus the non-overlapping condition. Also in this class there exists a unique invariant probability measure of full Hausdorff dimension. Finally we show that the Hausdorff dimension is a continuous function in this class.

We consider infinite Galton-Watson trees without leaves together with i.i.d.~random variables called marks on each of their vertices. We define a class of flow rules on marked Galton-Watson trees for which we are able, under some algebraic assumptions, to build explicit invariant measures. We apply this result, together with the ergodic theory on Galton-Watson trees developed in \cite{LPP95}, to the computation of Hausdorff dimensions of harmonic measures in two cases. The first one is the harmonic measure of the (transient) $λ$-biased random walk on Galton-Watson trees, for which the invariant measure and the dimension were not explicitly known. The second case is a model of random walk on a Galton-Watson trees with random lengths for which we compute the dimensions of the harmonic measure and show dimension drop phenomenon for the natural metric on the boundary and another metric that depends on the random lengths.

Considering that the maximum velocities for different types of vehicles ar e not the same and drivers may adopt different randomization brake probabilities , we pro posed a one_dimensional sensitive driving cellular automaton model for mixed tra ffic flow based on the NS model. We presented the fundamental diagrams for diff erent parameters by using computer numerical simulation, which show that the flu x is h igher than that of the NS model and the SDNS model. Moreover, some complicated b ehaviors of the model such as the metastable state and the separation of phases are also observed. Compared with the real traffic, the characters of this model are shown and analyzed.

The protocol followed was analogous to that by Garner and Ling [2014]. High spectral Entropy and high spectral Detrended Fluctuation Analysis have been established as more sensitive to the chaotic responses in forward problems in subjects with type 1 diabetes mellitus (T1DM), chronic obstructive pulmonary disease (COPD), youth obesity and child malnutrition. From 50 linearly increasing starting points, we applied the objective functions (hsCTF1 to hsCTF7) to attain an optimized value, [Formula: see text] which should increase the chaotic response when substituted back into the appropriate model. The mean percentage increase in Kolmogorov–Sinai Entropy and [Formula: see text]-values when the models were compared statistically by Kruskal–Wallis and One-way analysis of variance (ANOVA1) tests were monitored. Normal distributions were considered by Anderson–Darling, Ryan–Joiner and Lilliefors tests. Effect sizes by Cohen’ [Formula: see text] and Hedges [Formula: see text] were computed as sometimes the ANOVA1 and Kruskal–Wallis tests were not discriminative. The optimal statistical results were Duffing (hsCTF1: 29.4%); Brusselator (hsCTF6: 24.7%); Lorenz (hsCTF6: 28.8%). Multivariate analysis was required to determine which of the seven combinations of chaotic globals were most influential. Following Principal Component Analysis (PCA) hsCTF1 and hsCTF3 were the preferred objective functions delivering the highest increases in mean KS-Entropy with most influential PCA. These methods could be advantageous to increase yields and consequently profits in industrial processes such as yeast glycolysis for alcoholic beverages, Systems Biology, inverse and optimization problems, generally.

Conditional entropy of ordinal patterns