Ergodic Theory Via Joinings Mathematical Surveys and Monographs Volume 101
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Category Theory for Autonomous and Networked Dynamical Systems
Discussion Category Theory for Autonomous and Networked Dynamical Systems Jean-Charles Delvenne Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) and Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium; [email protected] Received: 7 February 2019; Accepted: 18 March 2019; Published: 20 March 2019 Abstract: In this discussion paper we argue that category theory may play a useful role in formulating, and perhaps proving, results in ergodic theory, topogical dynamics and open systems theory (control theory). As examples, we show how to characterize Kolmogorov–Sinai, Shannon entropy and topological entropy as the unique functors to the nonnegative reals satisfying some natural conditions. We also provide a purely categorical proof of the existence of the maximal equicontinuous factor in topological dynamics. We then show how to define open systems (that can interact with their environment), interconnect them, and define control problems for them in a unified way. Keywords: ergodic theory; topological dynamics; control theory 1. Introduction The theory of autonomous dynamical systems has developed in different flavours according to which structure is assumed on the state space—with topological dynamics (studying continuous maps on topological spaces) and ergodic theory (studying probability measures invariant under the map) as two prominent examples. These theories have followed parallel tracks and built multiple bridges. For example, both have grown successful tools from the interaction with information theory soon after its emergence, around the key invariants, namely the topological entropy and metric entropy, which are themselves linked by a variational theorem. The situation is more complex and heterogeneous in the field of what we here call open dynamical systems, or controlled dynamical systems. -
Effective S-Adic Symbolic Dynamical Systems
Effective S-adic symbolic dynamical systems Val´erieBerth´e,Thomas Fernique, and Mathieu Sablik? 1 IRIF, CNRS UMR 8243, Univ. Paris Diderot, France [email protected] 2 LIPN, CNRS UMR 7030, Univ. Paris 13, France [email protected] 3 I2M UMR 7373, Aix Marseille Univ., France [email protected] Abstract. We focus in this survey on effectiveness issues for S-adic sub- shifts and tilings. An S-adic subshift or tiling space is a dynamical system obtained by iterating an infinite composition of substitutions, where a substitution is a rule that replaces a letter by a word (that might be multi-dimensional), or a tile by a finite union of tiles. Several notions of effectiveness exist concerning S-adic subshifts and tiling spaces, such as the computability of the sequence of iterated substitutions, or the effec- tiveness of the language. We compare these notions and discuss effective- ness issues concerning classical properties of the associated subshifts and tiling spaces, such as the computability of shift-invariant measures and the existence of local rules (soficity). We also focus on planar tilings. Keywords: Symbolic dynamics; adic map; substitution; S-adic system; planar tiling; local rules; sofic subshift; subshift of finite type; computable invariant measure; effective language. 1 Introduction Decidability in symbolic dynamics and ergodic theory has already a long history. Let us quote as an illustration the undecidability of the emptiness problem (the domino problem) for multi-dimensional subshifts of finite type (SFT) [8, 40], or else the connections between effective ergodic theory, computable analysis and effective randomness (see for instance [14, 33, 44]). -
Ergodic Theory
MATH41112/61112 Ergodic Theory Charles Walkden 4th January, 2018 MATH4/61112 Contents Contents 0 Preliminaries 2 1 An introduction to ergodic theory. Uniform distribution of real se- quences 4 2 More on uniform distribution mod 1. Measure spaces. 13 3 Lebesgue integration. Invariant measures 23 4 More examples of invariant measures 38 5 Ergodic measures: definition, criteria, and basic examples 43 6 Ergodic measures: Using the Hahn-Kolmogorov Extension Theorem to prove ergodicity 53 7 Continuous transformations on compact metric spaces 62 8 Ergodic measures for continuous transformations 72 9 Recurrence 83 10 Birkhoff’s Ergodic Theorem 89 11 Applications of Birkhoff’s Ergodic Theorem 99 12 Solutions to the Exercises 108 1 MATH4/61112 0. Preliminaries 0. Preliminaries 0.1 Contact details § The lecturer is Dr Charles Walkden, Room 2.241, Tel: 0161 275 5805, Email: [email protected]. My office hour is: Monday 2pm-3pm. If you want to see me at another time then please email me first to arrange a mutually convenient time. 0.2 Course structure § This is a reading course, supported by one lecture per week. I have split the notes into weekly sections. You are expected to have read through the material before the lecture, and then go over it again afterwards in your own time. In the lectures I will highlight the most important parts, explain the statements of the theorems and what they mean in practice, and point out common misunderstandings. As a general rule, I will not normally go through the proofs in great detail (but they are examinable unless indicated otherwise). -
Topological Entropy and Distributional Chaos in Hereditary Shifts with Applications to Spacing Shifts and Beta Shifts
DISCRETE AND CONTINUOUS doi:10.3934/dcds.2013.33.2451 DYNAMICAL SYSTEMS Volume 33, Number 6, June 2013 pp. 2451{2467 TOPOLOGICAL ENTROPY AND DISTRIBUTIONAL CHAOS IN HEREDITARY SHIFTS WITH APPLICATIONS TO SPACING SHIFTS AND BETA SHIFTS Dedicated to the memory of Professor Andrzej Pelczar (1937-2010). Dominik Kwietniak Faculty of Mathematics and Computer Science Jagiellonian University in Krak´ow ul.Lojasiewicza 6, 30-348 Krak´ow,Poland (Communicated by Llu´ısAlsed`a) Abstract. Positive topological entropy and distributional chaos are charac- terized for hereditary shifts. A hereditary shift has positive topological entropy if and only if it is DC2-chaotic (or equivalently, DC3-chaotic) if and only if it is not uniquely ergodic. A hereditary shift is DC1-chaotic if and only if it is not proximal (has more than one minimal set). As every spacing shift and every beta shift is hereditary the results apply to those classes of shifts. Two open problems on topological entropy and distributional chaos of spacing shifts from an article of Banks et al. are solved thanks to this characterization. Moreover, it is shown that a spacing shift ΩP has positive topological entropy if and only if N n P is a set of Poincar´erecurrence. Using a result of Kˇr´ıˇzan example of a proximal spacing shift with positive entropy is constructed. Connections between spacing shifts and difference sets are revealed and the methods of this paper are used to obtain new proofs of some results on difference sets. 1. Introduction. A hereditary shift is a (one-sided) subshift X such that x 2 X and y ≤ x (coordinate-wise) imply y 2 X. -
Topological Dynamics of Ordinary Differential Equations and Kurzweil Equations*
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector JOURNAL OF DIFFERENTIAL EQUATIONS 23, 224-243 (1977) Topological Dynamics of Ordinary Differential Equations and Kurzweil Equations* ZVI ARTSTEIN Lefschetz Center for Dynamical Systems, Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912 Received March 25, 1975; revised September 4, 1975 1. INTRODUCTION A basic construction in applying topological dynamics to nonautonomous ordinary differential equations f =f(x, s) is to consider limit points (when 1 t 1 + co) of the translatesf, , wheref,(x, s) = f(~, t + s). A limit point defines a limiting equation and there is a tight relation- ship between the behavior of solutions of the original equation and of the limiting equations (see [l&13]). I n order to carry out this construction one has to specify the meaning of the convergence of ft , and this convergence has to have certain properties which we shall discuss later. In this paper we shall encounter a phenomenon which seems to be new in the context of applications of topological dynamics. The limiting equations will not be ordinary differential equations. We shall work under assumptions that do not guarantee that the space of ordinary equations is complete and we shall have to consider a completion of the space. Almost two decades ago Kurzweil [3] introduced what he called generalized ordinary differential equations and what we call Kurzweil equations. We shall see that if we embed our ordinary equations in a space of Kurzweil equations we get a complete and compact space, and the techniques of topological dynamics can be applied. -
Strictly Ergodic Symbolic Dynamical Systems
STRICTLY ERGODIC SYMBOLIC DYNAMICAL SYSTEMS SHIZUO KAKUTANI YALE UNIVERSITY 1. Introduction We continue the study of strictly ergodic symbolic dynamical systems which was started in our earlier report [6]. The main tools used in this investigation are "homomorphisms" and "substitutions". Among other things, we construct two strictly ergodic symbolic dynamical systems which are weakly mixing but not strongly mixing. 2. Strictly ergodic symbolic dynamical systems Let A be a finite set consisting of more than one element. Let (2.1) X = AZ = H A, An = A forallne Z, neZ be the set of all two sided infinite sequences (2.2) x = {a"In Z}, an = A for all n E Z, where (2.3) Z = {nln = O, + 1, + 2,} is the set of all integers. For each n E Z, an is called the nth coordinate of x, and the mapping (2.4) 7r,: x -+ a, = 7En(x) is called the nth projection of the power space X = AZ onto the base space An = A. The space X is a totally disconnected, compact, metrizable space with respect to the usual direct product topology. Let q be a one to one mapping of X = AZ onto itself defined by (2.5) 7En(q(X)) = ir.+1(X) for all n E Z. The mapping p is a homeomorphism ofX onto itself and is called the shift trans- formation. The dynamical system (X, (p) thus obtained is called the shift dynamical system. This research was supported in part by NSF Grant GP16392. 319 320 SIXTH BERKELEY SYMPOSIUM: KAKUTANI Let X0 be a nonempty closed subset of X which is invariant under (p. -
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. -
Conformal Fractals – Ergodic Theory Methods
Conformal Fractals – Ergodic Theory Methods Feliks Przytycki Mariusz Urba´nski May 17, 2009 2 Contents Introduction 7 0 Basic examples and definitions 15 1 Measure preserving endomorphisms 25 1.1 Measurespacesandmartingaletheorem . 25 1.2 Measure preserving endomorphisms, ergodicity . .. 28 1.3 Entropyofpartition ........................ 34 1.4 Entropyofendomorphism . 37 1.5 Shannon-Mcmillan-Breimantheorem . 41 1.6 Lebesguespaces............................ 44 1.7 Rohlinnaturalextension . 48 1.8 Generalized entropy, convergence theorems . .. 54 1.9 Countabletoonemaps.. .. .. .. .. .. .. .. .. .. 58 1.10 Mixingproperties . .. .. .. .. .. .. .. .. .. .. 61 1.11 Probabilitylawsand Bernoulliproperty . 63 Exercises .................................. 68 Bibliographicalnotes. 73 2 Compact metric spaces 75 2.1 Invariantmeasures . .. .. .. .. .. .. .. .. .. .. 75 2.2 Topological pressure and topological entropy . ... 83 2.3 Pressureoncompactmetricspaces . 87 2.4 VariationalPrinciple . 89 2.5 Equilibrium states and expansive maps . 94 2.6 Functionalanalysisapproach . 97 Exercises ..................................106 Bibliographicalnotes. 109 3 Distance expanding maps 111 3.1 Distance expanding open maps, basic properties . 112 3.2 Shadowingofpseudoorbits . 114 3.3 Spectral decomposition. Mixing properties . 116 3.4 H¨older continuous functions . 122 3 4 CONTENTS 3.5 Markov partitions and symbolic representation . 127 3.6 Expansive maps are expanding in some metric . 134 Exercises .................................. 136 Bibliographicalnotes. 138 4 -
Writing the History of Dynamical Systems and Chaos
Historia Mathematica 29 (2002), 273–339 doi:10.1006/hmat.2002.2351 Writing the History of Dynamical Systems and Chaos: View metadata, citation and similar papersLongue at core.ac.uk Dur´ee and Revolution, Disciplines and Cultures1 brought to you by CORE provided by Elsevier - Publisher Connector David Aubin Max-Planck Institut fur¨ Wissenschaftsgeschichte, Berlin, Germany E-mail: [email protected] and Amy Dahan Dalmedico Centre national de la recherche scientifique and Centre Alexandre-Koyre,´ Paris, France E-mail: [email protected] Between the late 1960s and the beginning of the 1980s, the wide recognition that simple dynamical laws could give rise to complex behaviors was sometimes hailed as a true scientific revolution impacting several disciplines, for which a striking label was coined—“chaos.” Mathematicians quickly pointed out that the purported revolution was relying on the abstract theory of dynamical systems founded in the late 19th century by Henri Poincar´e who had already reached a similar conclusion. In this paper, we flesh out the historiographical tensions arising from these confrontations: longue-duree´ history and revolution; abstract mathematics and the use of mathematical techniques in various other domains. After reviewing the historiography of dynamical systems theory from Poincar´e to the 1960s, we highlight the pioneering work of a few individuals (Steve Smale, Edward Lorenz, David Ruelle). We then go on to discuss the nature of the chaos phenomenon, which, we argue, was a conceptual reconfiguration as -
Turbulence, Entropy and Dynamics
TURBULENCE, ENTROPY AND DYNAMICS Lecture Notes, UPC 2014 Jose M. Redondo Contents 1 Turbulence 1 1.1 Features ................................................ 2 1.2 Examples of turbulence ........................................ 3 1.3 Heat and momentum transfer ..................................... 4 1.4 Kolmogorov’s theory of 1941 ..................................... 4 1.5 See also ................................................ 6 1.6 References and notes ......................................... 6 1.7 Further reading ............................................ 7 1.7.1 General ............................................ 7 1.7.2 Original scientific research papers and classic monographs .................. 7 1.8 External links ............................................. 7 2 Turbulence modeling 8 2.1 Closure problem ............................................ 8 2.2 Eddy viscosity ............................................. 8 2.3 Prandtl’s mixing-length concept .................................... 8 2.4 Smagorinsky model for the sub-grid scale eddy viscosity ....................... 8 2.5 Spalart–Allmaras, k–ε and k–ω models ................................ 9 2.6 Common models ........................................... 9 2.7 References ............................................... 9 2.7.1 Notes ............................................. 9 2.7.2 Other ............................................. 9 3 Reynolds stress equation model 10 3.1 Production term ............................................ 10 3.2 Pressure-strain interactions -
AN INTRODUCTION to DYNAMICAL BILLIARDS Contents 1
AN INTRODUCTION TO DYNAMICAL BILLIARDS SUN WOO PARK 2 Abstract. Some billiard tables in R contain crucial references to dynamical systems but can be analyzed with Euclidean geometry. In this expository paper, we will analyze billiard trajectories in circles, circular rings, and ellipses as well as relate their charactersitics to ergodic theory and dynamical systems. Contents 1. Background 1 1.1. Recurrence 1 1.2. Invariance and Ergodicity 2 1.3. Rotation 3 2. Dynamical Billiards 4 2.1. Circle 5 2.2. Circular Ring 7 2.3. Ellipse 9 2.4. Completely Integrable 14 Acknowledgments 15 References 15 Dynamical billiards exhibits crucial characteristics related to dynamical systems. Some billiard tables in R2 can be understood with Euclidean geometry. In this ex- pository paper, we will analyze some of the billiard tables in R2, specifically circles, circular rings, and ellipses. In the first section we will present some preliminary background. In the second section we will analyze billiard trajectories in the afore- mentioned billiard tables and relate their characteristics with dynamical systems. We will also briefly discuss the notion of completely integrable billiard mappings and Birkhoff's conjecture. 1. Background (This section follows Chapter 1 and 2 of Chernov [1] and Chapter 3 and 4 of Rudin [2]) In this section, we define basic concepts in measure theory and ergodic theory. We will focus on probability measures, related theorems, and recurrent sets on certain maps. The definitions of probability measures and σ-algebra are in Chapter 1 of Chernov [1]. 1.1. Recurrence. Definition 1.1. Let (X,A,µ) and (Y ,B,υ) be measure spaces. -
Groups and Topological Dynamics Volodymyr Nekrashevych
Groups and topological dynamics Volodymyr Nekrashevych E-mail address: [email protected] 2010 Mathematics Subject Classification. Primary ... ; Secondary ... Key words and phrases. .... The author ... Abstract. ... Contents Chapter 1. Dynamical systems 1 x1.1. Introduction by examples 1 x1.2. Subshifts 21 x1.3. Minimal Cantor systems 36 x1.4. Hyperbolic dynamics 74 x1.5. Holomorphic dynamics 103 Exercises 111 Chapter 2. Group actions 117 x2.1. Structure of orbits 117 x2.2. Localizable actions and Rubin's theorem 136 x2.3. Automata 149 x2.4. Groups acting on rooted trees 163 Exercises 193 Chapter 3. Groupoids 201 x3.1. Basic definitions 201 x3.2. Actions and correspondences 214 x3.3. Fundamental groups 233 x3.4. Orbispaces and complexes of groups 238 x3.5. Compactly generated groupoids 243 x3.6. Hyperbolic groupoids 252 Exercises 252 iii iv Contents Chapter 4. Iterated monodromy groups 255 x4.1. Iterated monodromy groups of self-coverings 255 x4.2. Self-similar groups 265 x4.3. General case 274 x4.4. Expanding maps and contracting groups 291 x4.5. Thurston maps and related structures 311 x4.6. Iterations of polynomials 333 x4.7. Functoriality 334 Exercises 340 Chapter 5. Groups from groupoids 345 x5.1. Full groups 345 x5.2. AF groupoids 364 x5.3. Homology of totally disconnected ´etalegroupoids 371 x5.4. Almost finite groupoids 378 x5.5. Bounded type 380 x5.6. Torsion groups 395 x5.7. Fragmentations of dihedral groups 401 x5.8. Purely infinite groupoids 423 Exercises 424 Chapter 6. Growth and amenability 427 x6.1. Growth of groups 427 x6.2.