Attractors and Orbit-Flip Homoclinic Orbits for Star Flows
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Dynamics Forced by Homoclinic Orbits
DYNAMICS FORCED BY HOMOCLINIC ORBITS VALENT´IN MENDOZA Abstract. The complexity of a dynamical system exhibiting a homoclinic orbit is given by the orbits that it forces. In this work we present a method, based in pruning theory, to determine the dynamical core of a homoclinic orbit of a Smale diffeomorphism on the 2-disk. Due to Cantwell and Conlon, this set is uniquely determined in the isotopy class of the orbit, up a topological conjugacy, so it contains the dynamics forced by the homoclinic orbit. Moreover we apply the method for finding the orbits forced by certain infinite families of homoclinic horseshoe orbits and propose its generalization to an arbitrary Smale map. 1. Introduction Since the Poincar´e'sdiscovery of homoclinic orbits, it is known that dynamical systems with one of these orbits have a very complex behaviour. Such a feature was explained by Smale in terms of his celebrated horseshoe map [40]; more precisely, if a surface diffeomorphism f has a homoclinic point then, there exists an invariant set Λ where a power of f is conjugated to the shift σ defined on the compact space Σ2 = f0; 1gZ of symbol sequences. To understand how complex is a diffeomorphism having a periodic or homoclinic orbit, we need the notion of forcing. First we will define it for periodic orbits. 1.1. Braid types of periodic orbits. Let P and Q be two periodic orbits of homeomorphisms f and g of the closed disk D2, respectively. We say that (P; f) and (Q; g) are equivalent if there is an orientation-preserving homeomorphism h : D2 ! D2 with h(P ) = Q such that f is isotopic to h−1 ◦ g ◦ h relative to P . -
Fractal Growth on the Surface of a Planet and in Orbit Around It
Wilfrid Laurier University Scholars Commons @ Laurier Physics and Computer Science Faculty Publications Physics and Computer Science 10-2014 Fractal Growth on the Surface of a Planet and in Orbit around It Ioannis Haranas Wilfrid Laurier University, [email protected] Ioannis Gkigkitzis East Carolina University, [email protected] Athanasios Alexiou Ionian University Follow this and additional works at: https://scholars.wlu.ca/phys_faculty Part of the Mathematics Commons, and the Physics Commons Recommended Citation Haranas, I., Gkigkitzis, I., Alexiou, A. Fractal Growth on the Surface of a Planet and in Orbit around it. Microgravity Sci. Technol. (2014) 26:313–325. DOI: 10.1007/s12217-014-9397-6 This Article is brought to you for free and open access by the Physics and Computer Science at Scholars Commons @ Laurier. It has been accepted for inclusion in Physics and Computer Science Faculty Publications by an authorized administrator of Scholars Commons @ Laurier. For more information, please contact [email protected]. 1 Fractal Growth on the Surface of a Planet and in Orbit around it 1Ioannis Haranas, 2Ioannis Gkigkitzis, 3Athanasios Alexiou 1Dept. of Physics and Astronomy, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada 2Departments of Mathematics and Biomedical Physics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC 27858-4353, USA 3Department of Informatics, Ionian University, Plateia Tsirigoti 7, Corfu, 49100, Greece Abstract: Fractals are defined as geometric shapes that exhibit symmetry of scale. This simply implies that fractal is a shape that it would still look the same even if somebody could zoom in on one of its parts an infinite number of times. -
CURRENT EVENTS BULLETIN Friday, January 8, 2016, 1:00 PM to 5:00 PM Room 4C-3 Washington State Convention Center Joint Mathematics Meetings, Seattle, WA
A MERICAN M ATHEMATICAL S OCIETY CURRENT EVENTS BULLETIN Friday, January 8, 2016, 1:00 PM to 5:00 PM Room 4C-3 Washington State Convention Center Joint Mathematics Meetings, Seattle, WA 1:00 PM Carina Curto, Pennsylvania State University What can topology tell us about the neural code? Surprising new applications of what used to be thought of as “pure” mathematics. 2:00 PM Yuval Peres, Microsoft Research and University of California, Berkeley, and Lionel Levine, Cornell University Laplacian growth, sandpiles and scaling limits Striking large-scale structure arising from simple cellular automata. 3:00 PM Timothy Gowers, Cambridge University Probabilistic combinatorics and the recent work of Peter Keevash The major existence conjecture for combinatorial designs has been proven! 4:00 PM Amie Wilkinson, University of Chicago What are Lyapunov exponents, and why are they interesting? A basic tool in understanding the predictability of physical systems, explained. Organized by David Eisenbud, Mathematical Sciences Research Institute Introduction to the Current Events Bulletin Will the Riemann Hypothesis be proved this week? What is the Geometric Langlands Conjecture about? How could you best exploit a stream of data flowing by too fast to capture? I think we mathematicians are provoked to ask such questions by our sense that underneath the vastness of mathematics is a fundamental unity allowing us to look into many different corners -- though we couldn't possibly work in all of them. I love the idea of having an expert explain such things to me in a brief, accessible way. And I, like most of us, love common-room gossip. -
I. Overview of Activities, April, 2005-March, 2006 …
MATHEMATICAL SCIENCES RESEARCH INSTITUTE ANNUAL REPORT FOR 2005-2006 I. Overview of Activities, April, 2005-March, 2006 …......……………………. 2 Innovations ………………………………………………………..... 2 Scientific Highlights …..…………………………………………… 4 MSRI Experiences ….……………………………………………… 6 II. Programs …………………………………………………………………….. 13 III. Workshops ……………………………………………………………………. 17 IV. Postdoctoral Fellows …………………………………………………………. 19 Papers by Postdoctoral Fellows …………………………………… 21 V. Mathematics Education and Awareness …...………………………………. 23 VI. Industrial Participation ...…………………………………………………… 26 VII. Future Programs …………………………………………………………….. 28 VIII. Collaborations ………………………………………………………………… 30 IX. Papers Reported by Members ………………………………………………. 35 X. Appendix - Final Reports ……………………………………………………. 45 Programs Workshops Summer Graduate Workshops MSRI Network Conferences MATHEMATICAL SCIENCES RESEARCH INSTITUTE ANNUAL REPORT FOR 2005-2006 I. Overview of Activities, April, 2005-March, 2006 This annual report covers MSRI projects and activities that have been concluded since the submission of the last report in May, 2005. This includes the Spring, 2005 semester programs, the 2005 summer graduate workshops, the Fall, 2005 programs and the January and February workshops of Spring, 2006. This report does not contain fiscal or demographic data. Those data will be submitted in the Fall, 2006 final report covering the completed fiscal 2006 year, based on audited financial reports. This report begins with a discussion of MSRI innovations undertaken this year, followed by highlights -
What Are Lyapunov Exponents, and Why Are They Interesting?
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 54, Number 1, January 2017, Pages 79–105 http://dx.doi.org/10.1090/bull/1552 Article electronically published on September 6, 2016 WHAT ARE LYAPUNOV EXPONENTS, AND WHY ARE THEY INTERESTING? AMIE WILKINSON Introduction At the 2014 International Congress of Mathematicians in Seoul, South Korea, Franco-Brazilian mathematician Artur Avila was awarded the Fields Medal for “his profound contributions to dynamical systems theory, which have changed the face of the field, using the powerful idea of renormalization as a unifying principle.”1 Although it is not explicitly mentioned in this citation, there is a second unify- ing concept in Avila’s work that is closely tied with renormalization: Lyapunov (or characteristic) exponents. Lyapunov exponents play a key role in three areas of Avila’s research: smooth ergodic theory, billiards and translation surfaces, and the spectral theory of 1-dimensional Schr¨odinger operators. Here we take the op- portunity to explore these areas and reveal some underlying themes connecting exponents, chaotic dynamics and renormalization. But first, what are Lyapunov exponents? Let’s begin by viewing them in one of their natural habitats: the iterated barycentric subdivision of a triangle. When the midpoint of each side of a triangle is connected to its opposite vertex by a line segment, the three resulting segments meet in a point in the interior of the triangle. The barycentric subdivision of a triangle is the collection of 6 smaller triangles determined by these segments and the edges of the original triangle: Figure 1. Barycentric subdivision. Received by the editors August 2, 2016. -
An Image Cryptography Using Henon Map and Arnold Cat Map
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072 An Image Cryptography using Henon Map and Arnold Cat Map. Pranjali Sankhe1, Shruti Pimple2, Surabhi Singh3, Anita Lahane4 1,2,3 UG Student VIII SEM, B.E., Computer Engg., RGIT, Mumbai, India 4Assistant Professor, Department of Computer Engg., RGIT, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this digital world i.e. the transmission of non- 2. METHODOLOGY physical data that has been encoded digitally for the purpose of storage Security is a continuous process via which data can 2.1 HENON MAP be secured from several active and passive attacks. Encryption technique protects the confidentiality of a message or 1. The Henon map is a discrete time dynamic system information which is in the form of multimedia (text, image, introduces by michel henon. and video).In this paper, a new symmetric image encryption 2. The map depends on two parameters, a and b, which algorithm is proposed based on Henon’s chaotic system with for the classical Henon map have values of a = 1.4 and byte sequences applied with a novel approach of pixel shuffling b = 0.3. For the classical values the Henon map is of an image which results in an effective and efficient chaotic. For other values of a and b the map may be encryption of images. The Arnold Cat Map is a discrete system chaotic, intermittent, or converge to a periodic orbit. that stretches and folds its trajectories in phase space. Cryptography is the process of encryption and decryption of 3. -
WHAT IS a CHAOTIC ATTRACTOR? 1. Introduction J. Yorke Coined the Word 'Chaos' As Applied to Deterministic Systems. R. Devane
WHAT IS A CHAOTIC ATTRACTOR? CLARK ROBINSON Abstract. Devaney gave a mathematical definition of the term chaos, which had earlier been introduced by Yorke. We discuss issues involved in choosing the properties that characterize chaos. We also discuss how this term can be combined with the definition of an attractor. 1. Introduction J. Yorke coined the word `chaos' as applied to deterministic systems. R. Devaney gave the first mathematical definition for a map to be chaotic on the whole space where a map is defined. Since that time, there have been several different definitions of chaos which emphasize different aspects of the map. Some of these are more computable and others are more mathematical. See [9] a comparison of many of these definitions. There is probably no one best or correct definition of chaos. In this paper, we discuss what we feel is one of better mathematical definition. (It may not be as computable as some of the other definitions, e.g., the one by Alligood, Sauer, and Yorke.) Our definition is very similar to the one given by Martelli in [8] and [9]. We also combine the concepts of chaos and attractors and discuss chaotic attractors. 2. Basic definitions We start by giving the basic definitions needed to define a chaotic attractor. We give the definitions for a diffeomorphism (or map), but those for a system of differential equations are similar. The orbit of a point x∗ by F is the set O(x∗; F) = f Fi(x∗) : i 2 Z g. An invariant set for a diffeomorphism F is an set A in the domain such that F(A) = A. -
Boshernitzan's Condition, Factor Complexity, and an Application
BOSHERNITZAN’S CONDITION, FACTOR COMPLEXITY, AND AN APPLICATION VAN CYR AND BRYNA KRA Abstract. Boshernitzan found a decay condition on the measure of cylin- der sets that implies unique ergodicity for minimal subshifts. Interest in the properties of subshifts satisfying this condition has grown recently, due to a connection with the study of discrete Schr¨odinger operators. Of particular interest is the question of how restrictive Boshernitzan’s condition is. While it implies zero topological entropy, our main theorem shows how to construct minimal subshifts satisfying the condition whose factor complexity grows faster than any pre-assigned subexponential rate. As an application, via a theorem of Damanik and Lenz, we show that there is no subexponentially growing se- quence for which the spectra of all discrete Schr¨odinger operators associated with subshifts whose complexity grows faster than the given sequence, have only finitely many gaps. 1. Boshernitzan’s complexity conditions For a symbolic dynamical system (X, σ), many of the isomorphism invariants we have are statements about the growth rate of the word complexity function PX (n), which counts the number of distinct cylinder sets determined by words of length n having nonempty intersection with X. For example, the exponential growth of PX (n) is the topological entropy of (X, σ), while the linear growth rate of PX (n) gives an invariant to begin distinguishing between zero entropy systems. Of course there are different senses in which the growth of PX (n) could be said to be linear and different invariants arise from them. For example, one can consider systems with linear limit inferior growth, meaning lim infn→∞ PX (n)/n < ∞, or the stronger condition of linear limit superior growth, meaning lim supn→∞ PX (n)/n < ∞. -
An Example of Bifurcation to Homoclinic Orbits
JOURNAL OF DIFFERENTIAL EQUATIONS 37, 351-373 (1980) An Example of Bifurcation to Homoclinic Orbits SHUI-NEE CHOW* Department of Mathematics, Michigan State University, East Lansing, Michigan 48823 AND JACK K. HALE+ AND JOHN MALLET-PARET Lefschetz Center for Dynamical Systems, Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912 Received October 19, 1979; revised January 23, 1980 Consider the equation f - x + x 2 = -A12 + &f(t) where f(t + 1) = f(t) and h = (h, , A,) is small. For h = 0, there is a homoclinic orbit r through zero. For X # 0 and small, there can be “strange” attractors near r. The pur- pose of this paper is to determine the curves in /\-space of bifurcation to “strange” attractors and to relate this to hyperbolic subharmonic bifurcations. I. INTRODUCTION At the present time, there is considerable interest in the theory of “strange” attractors of mappings and the manner in which strange attractors arise through successive bifurcations of periodic orbits (see [l, 5-121). The simplest “strange” attractors arise near homoclinic points. For a hyperbolic fixed point p of a diffeomorphism, a point 4 # p is a homo- clinic point of p if 4 is in the intersection of the stable and unstable manifolds of p. The point 4 is called a transverse homoclinic point of p if the intersection of the stable and unstable manifolds of p at 4 is transversal. It was known to Poincare that the existence of one transverse homoclinic point implies there must be infinitely many transverse homoclinic points. Birkhoff showed that every * This research was supported in part by the Netional Science Foundation, under MCS76-06739. -
MA427 Ergodic Theory
MA427 Ergodic Theory Course Notes (2012-13) 1 Introduction 1.1 Orbits Let X be a mathematical space. For example, X could be the unit interval [0; 1], a circle, a torus, or something far more complicated like a Cantor set. Let T : X ! X be a function that maps X into itself. Let x 2 X be a point. We can repeatedly apply the map T to the point x to obtain the sequence: fx; T (x);T (T (x));T (T (T (x))); : : : ; :::g: We will often write T n(x) = T (··· (T (T (x)))) (n times). The sequence of points x; T (x);T 2(x);::: is called the orbit of x. We think of applying the map T as the passage of time. Thus we think of T (x) as where the point x has moved to after time 1, T 2(x) is where the point x has moved to after time 2, etc. Some points x 2 X return to where they started. That is, T n(x) = x for some n > 1. We say that such a point x is periodic with period n. By way of contrast, points may move move densely around the space X. (A sequence is said to be dense if (loosely speaking) it comes arbitrarily close to every point of X.) If we take two points x; y of X that start very close then their orbits will initially be close. However, it often happens that in the long term their orbits move apart and indeed become dramatically different. -
Codimension-Two Homoclinic Bifurcations Underlying Spike Adding in the Hindmarsh-Rose Burster Arxiv:1109.5689V1 [Math.DS] 26 S
Codimension-two homoclinic bifurcations underlying spike adding in the Hindmarsh-Rose burster Daniele Linaro1, Alan Champneys2, Mathieu Desroches2, Marco Storace1 1Biophysical and Electronic Engineering Department, University of Genoa, Genova, Italy 2Department of Engineering Mathematics, University of Bristol, Bristol, UK July 2, 2018 Abstract The well-studied Hindmarsh-Rose model of neural action potential is revisited from the point of view of global bifurcation analysis. This slow-fast system of three paremeterised differential equations is arguably the simplest reduction of Hodgkin- Huxley models capable of exhibiting all qualitatively important distinct kinds of spiking and bursting behaviour. First, keeping the singular perturbation parameter fixed, a comprehensive two-parameter bifurcation diagram is computed by brute force. Of particular concern is the parameter regime where lobe-shaped regions of irregular bursting undergo a transition to stripe-shaped regions of periodic bursting. The boundary of each stripe represents a fold bifurcation that causes a smooth spike-adding transition where the number of spikes in each burst is increased by one. Next, numerical continuation studies reveal that the global structure is organised by various curves of homoclinic bifurcations. In particular the lobe to stripe transition is organised by a sequence of codimension- two orbit- and inclination-flip points that occur along each homoclinic branch. Each branch undergoes a sharp turning point and hence approximately has a double- cover of the same curve in parameter space. The sharp turn is explained in terms of the interaction between a two-dimensional unstable manifold and a one-dimensional slow manifold in the singular limit. Finally, a new local analysis is undertaken us- ing approximate Poincar´emaps to show that the turning point on each homoclinic branch in turn induces an inclination flip that gives birth to the fold curve that or- ganises the spike-adding transition. -
ORBIT EQUIVALENCE of ERGODIC GROUP ACTIONS These Are Partial Lecture Notes from a Topics Graduate Class Taught at UCSD in Fall 2
1 ORBIT EQUIVALENCE OF ERGODIC GROUP ACTIONS ADRIAN IOANA These are partial lecture notes from a topics graduate class taught at UCSD in Fall 2019. 1. Group actions, equivalence relations and orbit equivalence 1.1. Standard spaces. A Borel space (X; BX ) is a pair consisting of a topological space X and its σ-algebra of Borel sets BX . If the topology on X can be chosen to the Polish (i.e., separable, metrizable and complete), then (X; BX ) is called a standard Borel space. For simplicity, we will omit the σ-algebra and write X instead of (X; BX ). If (X; BX ) and (Y; BY ) are Borel spaces, then a map θ : X ! Y is called a Borel isomorphism if it is a bijection and θ and θ−1 are Borel measurable. Note that a Borel bijection θ is automatically a Borel isomorphism (see [Ke95, Corollary 15.2]). Definition 1.1. A standard probability space is a measure space (X; BX ; µ), where (X; BX ) is a standard Borel space and µ : BX ! [0; 1] is a σ-additive measure with µ(X) = 1. For simplicity, we will write (X; µ) instead of (X; BX ; µ). If (X; µ) and (Y; ν) are standard probability spaces, a map θ : X ! Y is called a measure preserving (m.p.) isomorphism if it is a Borel isomorphism and −1 measure preserving: θ∗µ = ν, where θ∗µ(A) = µ(θ (A)), for any A 2 BY . Example 1.2. The following are standard probability spaces: (1) ([0; 1]; λ), where [0; 1] is endowed with the Euclidean topology and its Lebegue measure λ.