Lectures on Fractal Geometry and Dynamical Systems
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Synchronization of Chaotic Systems
Synchronization of chaotic systems Cite as: Chaos 25, 097611 (2015); https://doi.org/10.1063/1.4917383 Submitted: 08 January 2015 . Accepted: 18 March 2015 . Published Online: 16 April 2015 Louis M. Pecora, and Thomas L. Carroll ARTICLES YOU MAY BE INTERESTED IN Nonlinear time-series analysis revisited Chaos: An Interdisciplinary Journal of Nonlinear Science 25, 097610 (2015); https:// doi.org/10.1063/1.4917289 Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data Chaos: An Interdisciplinary Journal of Nonlinear Science 27, 121102 (2017); https:// doi.org/10.1063/1.5010300 Attractor reconstruction by machine learning Chaos: An Interdisciplinary Journal of Nonlinear Science 28, 061104 (2018); https:// doi.org/10.1063/1.5039508 Chaos 25, 097611 (2015); https://doi.org/10.1063/1.4917383 25, 097611 © 2015 Author(s). CHAOS 25, 097611 (2015) Synchronization of chaotic systems Louis M. Pecora and Thomas L. Carroll U.S. Naval Research Laboratory, Washington, District of Columbia 20375, USA (Received 8 January 2015; accepted 18 March 2015; published online 16 April 2015) We review some of the history and early work in the area of synchronization in chaotic systems. We start with our own discovery of the phenomenon, but go on to establish the historical timeline of this topic back to the earliest known paper. The topic of synchronization of chaotic systems has always been intriguing, since chaotic systems are known to resist synchronization because of their positive Lyapunov exponents. The convergence of the two systems to identical trajectories is a surprise. We show how people originally thought about this process and how the concept of synchronization changed over the years to a more geometric view using synchronization manifolds. -
Multiple Mixing from Weak Hyperbolicity by the Hopf Argument Yves Coudène, Boris Hasselblatt, Serge Troubetzkoy
,pdfcreator=HAL,pdfproducer=PDFLaTeX,pdfsubject=Mathematics [math]/Dynamical Systems [math.DS] Multiple mixing from weak hyperbolicity by the Hopf argument Yves Coudène, Boris Hasselblatt, Serge Troubetzkoy To cite this version: Yves Coudène, Boris Hasselblatt, Serge Troubetzkoy. Multiple mixing from weak hyperbolicity by the Hopf argument. 2014. hal-01006451v2 HAL Id: hal-01006451 https://hal.archives-ouvertes.fr/hal-01006451v2 Submitted on 16 Jun 2014 (v2), last revised 15 Sep 2015 (v3) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. MULTIPLE MIXING FROM WEAK HYPERBOLICITY BY THE HOPF ARGUMENT YVES COUDÈNE, BORIS HASSELBLATT AND SERGE TROUBETZKOY ABSTRACT. We show that using only weak hyperbolicity (no smoothness, com- pactness or exponential rates) the Hopf argument produces multiple mixing in an elementary way. While this recovers classical results with far simpler proofs, the point is the broader applicability implied by the weak hypothe- ses. Some of the results can also be viewed as establishing “mixing implies multiple mixing” outside the classical hyperbolic context. 1. INTRODUCTION The origins of hyperbolic dynamical systems are connected with the efforts by Boltzmann and Maxwell to lay a foundation under statistical mechanics. In today’s terms their ergodic hypothesis was that the mechanical system defined by molecules in a container is ergodic, and the difficulties of establishing this led to the search for any mechanical systems with this property. -
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]). -
Diagonalizable Flows on Locally Homogeneous Spaces and Number
Diagonalizable flows on locally homogeneous spaces and number theory Manfred Einsiedler and Elon Lindenstrauss∗ Abstract.We discuss dynamical properties of actions of diagonalizable groups on locally homogeneous spaces, particularly their invariant measures, and present some number theoretic and spectral applications. Entropy plays a key role in the study of theses invariant measures and in the applications. Mathematics Subject Classification (2000). 37D40, 37A45, 11J13, 81Q50 Keywords. invariant measures, locally homogeneous spaces, Littlewood’s conjecture, quantum unique ergodicity, distribution of periodic orbits, ideal classes, entropy. 1. Introduction Flows on locally homogeneous spaces are a special kind of dynamical systems. The ergodic theory and dynamics of these flows are very rich and interesting, and their study has a long and distinguished history. What is more, this study has found numerous applications throughout mathematics. The spaces we consider are of the form Γ\G where G is a locally compact group and Γ a discrete subgroup of G. Typically one takes G to be either a Lie group, a linear algebraic group over a local field, or a product of such. Any subgroup H < G acts on Γ\G and this action is precisely the type of action we will consider here. One of the most important examples which features in numerous number theoretical applications is the space PGL(n, Z)\ PGL(n, R) which can be identified with the space of lattices in Rn up to homothety. Part of the beauty of the subject is that the study of very concrete actions can have meaningful implications. For example, in the late 1980s G. -
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. -
Learning from Benoit Mandelbrot
2/28/18, 7(59 AM Page 1 of 1 February 27, 2018 Learning from Benoit Mandelbrot If you've been reading some of my recent posts, you will have noted my, and the Investment Masters belief, that many of the investment theories taught in most business schools are flawed. And they can be dangerous too, the recent Financial Crisis is evidence of as much. One man who, prior to the Financial Crisis, issued a challenge to regulators including the Federal Reserve Chairman Alan Greenspan, to recognise these flaws and develop more realistic risk models was Benoit Mandelbrot. Over the years I've read a lot of investment books, and the name Benoit Mandelbrot comes up from time to time. I recently finished his book 'The (Mis)Behaviour of Markets - A Fractal View of Risk, Ruin and Reward'. So who was Benoit Mandelbrot, why is he famous, and what can we learn from him? Benoit Mandelbrot was a Polish-born mathematician and polymath, a Sterling Professor of Mathematical Sciences at Yale University and IBM Fellow Emeritus (Physics) who developed a new branch of mathematics known as 'Fractal' geometry. This geometry recognises the hidden order in the seemingly disordered, the plan in the unplanned, the regular pattern in the irregularity and roughness of nature. It has been successfully applied to the natural sciences helping model weather, study river flows, analyse brainwaves and seismic tremors. A 'fractal' is defined as a rough or fragmented shape that can be split into parts, each of which is at least a close approximation of its original-self. -
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 -
Role of Nonlinear Dynamics and Chaos in Applied Sciences
v.;.;.:.:.:.;.;.^ ROLE OF NONLINEAR DYNAMICS AND CHAOS IN APPLIED SCIENCES by Quissan V. Lawande and Nirupam Maiti Theoretical Physics Oivisipn 2000 Please be aware that all of the Missing Pages in this document were originally blank pages BARC/2OOO/E/OO3 GOVERNMENT OF INDIA ATOMIC ENERGY COMMISSION ROLE OF NONLINEAR DYNAMICS AND CHAOS IN APPLIED SCIENCES by Quissan V. Lawande and Nirupam Maiti Theoretical Physics Division BHABHA ATOMIC RESEARCH CENTRE MUMBAI, INDIA 2000 BARC/2000/E/003 BIBLIOGRAPHIC DESCRIPTION SHEET FOR TECHNICAL REPORT (as per IS : 9400 - 1980) 01 Security classification: Unclassified • 02 Distribution: External 03 Report status: New 04 Series: BARC External • 05 Report type: Technical Report 06 Report No. : BARC/2000/E/003 07 Part No. or Volume No. : 08 Contract No.: 10 Title and subtitle: Role of nonlinear dynamics and chaos in applied sciences 11 Collation: 111 p., figs., ills. 13 Project No. : 20 Personal authors): Quissan V. Lawande; Nirupam Maiti 21 Affiliation ofauthor(s): Theoretical Physics Division, Bhabha Atomic Research Centre, Mumbai 22 Corporate authoifs): Bhabha Atomic Research Centre, Mumbai - 400 085 23 Originating unit : Theoretical Physics Division, BARC, Mumbai 24 Sponsors) Name: Department of Atomic Energy Type: Government Contd...(ii) -l- 30 Date of submission: January 2000 31 Publication/Issue date: February 2000 40 Publisher/Distributor: Head, Library and Information Services Division, Bhabha Atomic Research Centre, Mumbai 42 Form of distribution: Hard copy 50 Language of text: English 51 Language of summary: English 52 No. of references: 40 refs. 53 Gives data on: Abstract: Nonlinear dynamics manifests itself in a number of phenomena in both laboratory and day to day dealings. -
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 -
Mild Vs. Wild Randomness: Focusing on Those Risks That Matter
with empirical sense1. Granted, it has been Mild vs. Wild Randomness: tinkered with using such methods as Focusing on those Risks that complementary “jumps”, stress testing, regime Matter switching or the elaborate methods known as GARCH, but while they represent a good effort, Benoit Mandelbrot & Nassim Nicholas Taleb they fail to remediate the bell curve’s irremediable flaws. Forthcoming in, The Known, the Unknown and the The problem is that measures of uncertainty Unknowable in Financial Institutions Frank using the bell curve simply disregard the possibility Diebold, Neil Doherty, and Richard Herring, of sharp jumps or discontinuities. Therefore they editors, Princeton: Princeton University Press. have no meaning or consequence. Using them is like focusing on the grass and missing out on the (gigantic) trees. Conventional studies of uncertainty, whether in statistics, economics, finance or social science, In fact, while the occasional and unpredictable have largely stayed close to the so-called “bell large deviations are rare, they cannot be dismissed curve”, a symmetrical graph that represents a as “outliers” because, cumulatively, their impact in probability distribution. Used to great effect to the long term is so dramatic. describe errors in astronomical measurement by The good news, especially for practitioners, is the 19th-century mathematician Carl Friedrich that the fractal model is both intuitively and Gauss, the bell curve, or Gaussian model, has computationally simpler than the Gaussian. It too since pervaded our business and scientific culture, has been around since the sixties, which makes us and terms like sigma, variance, standard deviation, wonder why it was not implemented before. correlation, R-square and Sharpe ratio are all The traditional Gaussian way of looking at the directly linked to it. -
Bézier Curves Are Attractors of Iterated Function Systems
New York Journal of Mathematics New York J. Math. 13 (2007) 107–115. All B´ezier curves are attractors of iterated function systems Chand T. John Abstract. The fields of computer aided geometric design and fractal geom- etry have evolved independently of each other over the past several decades. However, the existence of so-called smooth fractals, i.e., smooth curves or sur- faces that have a self-similar nature, is now well-known. Here we describe the self-affine nature of quadratic B´ezier curves in detail and discuss how these self-affine properties can be extended to other types of polynomial and ra- tional curves. We also show how these properties can be used to control shape changes in complex fractal shapes by performing simple perturbations to smooth curves. Contents 1. Introduction 107 2. Quadratic B´ezier curves 108 3. Iterated function systems 109 4. An IFS with a QBC attractor 110 5. All QBCs are attractors of IFSs 111 6. Controlling fractals with B´ezier curves 112 7. Conclusion and future work 114 References 114 1. Introduction In the late 1950s, advancements in hardware technology made it possible to effi- ciently manufacture curved 3D shapes out of blocks of wood or steel. It soon became apparent that the bottleneck in mass production of curved 3D shapes was the lack of adequate software for designing these shapes. B´ezier curves were first introduced in the 1960s independently by two engineers in separate French automotive compa- nies: first by Paul de Casteljau at Citro¨en, and then by Pierre B´ezier at R´enault. -
Symbolic Dynamics and Markov Partitions
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 35, Number 1, January 1998, Pages 1–56 S 0273-0979(98)00737-X SYMBOLIC DYNAMICS AND MARKOV PARTITIONS ROY L. ADLER Abstract. The decimal expansion of real numbers, familiar to us all, has a dramatic generalization to representation of dynamical system orbits by sym- bolic sequences. The natural way to associate a symbolic sequence with an orbit is to track its history through a partition. But in order to get a useful symbolism, one needs to construct a partition with special properties. In this work we develop a general theory of representing dynamical systems by sym- bolic systems by means of so-called Markov partitions. We apply the results to one of the more tractable examples: namely, hyperbolic automorphisms of the two dimensional torus. While there are some results in higher dimensions, this area remains a fertile one for research. 1. Introduction We address the question: how and to what extent can a dynamical system be represented by a symbolic one? The first use of infinite sequences of symbols to describe orbits is attributed to a nineteenth century work of Hadamard [H]. How- ever the present work is rooted in something very much older and familiar to us all: namely, the representation of real numbers by infinite binary expansions. As the example of Section 3.2 shows, such a representation is related to the behavior of a special partition under the action of a map. Partitions of this sort have been linked to the name Markov because of their connection to discrete time Markov processes.