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THE LORENZ SYSTEM Math118, O. Knill GLOBAL EXISTENCE
THE LORENZ SYSTEM Math118, O. Knill GLOBAL EXISTENCE. Remember that nonlinear differential equations do not necessarily have global solutions like d=dtx(t) = x2(t). If solutions do not exist for all times, there is a finite τ such that x(t) for t τ. j j ! 1 ! ABSTRACT. In this lecture, we have a closer look at the Lorenz system. LEMMA. The Lorenz system has a solution x(t) for all times. THE LORENZ SYSTEM. The differential equations Since we have a trapping region, the Lorenz differential equation exist for all times t > 0. If we run time _ 2 2 2 ct x_ = σ(y x) backwards, we have V = 2σ(rx + y + bz 2brz) cV for some constant c. Therefore V (t) V (0)e . − − ≤ ≤ y_ = rx y xz − − THE ATTRACTING SET. The set K = t> Tt(E) is invariant under the differential equation. It has zero z_ = xy bz : T 0 − volume and is called the attracting set of the Lorenz equations. It contains the unstable manifold of O. are called the Lorenz system. There are three parameters. For σ = 10; r = 28; b = 8=3, Lorenz discovered in 1963 an interesting long time behavior and an EQUILIBRIUM POINTS. Besides the origin O = (0; 0; 0, we have two other aperiodic "attractor". The picture to the right shows a numerical integration equilibrium points. C = ( b(r 1); b(r 1); r 1). For r < 1, all p − p − − of an orbit for t [0; 40]. solutions are attracted to the origin. At r = 1, the two equilibrium points 2 appear with a period doubling bifurcation. -
Chaos Theory
By Nadha CHAOS THEORY What is Chaos Theory? . It is a field of study within applied mathematics . It studies the behavior of dynamical systems that are highly sensitive to initial conditions . It deals with nonlinear systems . It is commonly referred to as the Butterfly Effect What is a nonlinear system? . In mathematics, a nonlinear system is a system which is not linear . It is a system which does not satisfy the superposition principle, or whose output is not directly proportional to its input. Less technically, a nonlinear system is any problem where the variable(s) to be solved for cannot be written as a linear combination of independent components. the SUPERPOSITION PRINCIPLE states that, for all linear systems, the net response at a given place and time caused by two or more stimuli is the sum of the responses which would have been caused by each stimulus individually. So that if input A produces response X and input B produces response Y then input (A + B) produces response (X + Y). So a nonlinear system does not satisfy this principal. There are 3 criteria that a chaotic system satisfies 1) sensitive dependence on initial conditions 2) topological mixing 3) periodic orbits are dense 1) Sensitive dependence on initial conditions . This is popularly known as the "butterfly effect” . It got its name because of the title of a paper given by Edward Lorenz titled Predictability: Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas? . The flapping wing represents a small change in the initial condition of the system, which causes a chain of events leading to large-scale phenomena. -
Synchronization in Nonlinear Systems and Networks
Synchronization in Nonlinear Systems and Networks Yuri Maistrenko E-mail: [email protected] you can find me in the room EW 632, Wednesday 13:00-14:00 Lecture 4 - 23.11.2011 1 Chaos actually … is everywhere Web Book CHAOS = BUTTERFLY EFFECT Henri Poincaré (1880) “ It so happens that small differences in the initial state of the system can lead to very large differences in its final state. A small error in the former could then produce an enormous one in the latter. Prediction becomes impossible, and the system appears to behave randomly.” Ray Bradbury “A Sound of Thunder “ (1952) THE ESSENCE OF CHAOS • processes deterministic fully determined by initial state • long-term behavior unpredictable butterfly effect PHYSICAL “DEFINITION “ OF CHAOS “To say that a certain system exhibits chaos means that the system obeys deterministic law of evolution but that the outcome is highly sensitive to small uncertainties in the specification of the initial state. In chaotic system any open ball of initial conditions, no matter how small, will in finite time spread over the extent of the entire asymptotically admissible phase space” Predrag Cvitanovich . Appl.Chaos 1992 EXAMPLES OF CHAOTIC SYSTEMS • the solar system (Poincare) • the weather (Lorenz) • turbulence in fluids • population growth • lots and lots of other systems… “HOT” APPLICATIONS • neuronal networks of the brain • genetic networks UNPREDICTIBILITY OF THE WEATHER Edward Lorenz (1963) Difficulties in predicting the weather are not related to the complexity of the Earths’ climate but to CHAOS in the climate equations! Dynamical systems Dynamical system: a system of one or more variables which evolve in time according to a given rule Two types of dynamical systems: • Differential equations: time is continuous (called flow) dx N f (x), t R dt • Difference equations (iterated maps): time is discrete (called cascade) xn1 f (xn ), n 0, 1, 2,.. -
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 -
Annotated List of References Tobias Keip, I7801986 Presentation Method: Poster
Personal Inquiry – Annotated list of references Tobias Keip, i7801986 Presentation Method: Poster Poster Section 1: What is Chaos? In this section I am introducing the topic. I am describing different types of chaos and how individual perception affects our sense for chaos or chaotic systems. I am also going to define the terminology. I support my ideas with a lot of examples, like chaos in our daily life, then I am going to do a transition to simple mathematical chaotic systems. Larry Bradley. (2010). Chaos and Fractals. Available: www.stsci.edu/~lbradley/seminar/. Last accessed 13 May 2010. This website delivered me with a very good introduction into the topic as there are a lot of books and interesting web-pages in the “References”-Sektion. Gleick, James. Chaos: Making a New Science. Penguin Books, 1987. The book gave me a very general introduction into the topic. Harald Lesch. (2003-2007). alpha-Centauri . Available: www.br-online.de/br- alpha/alpha-centauri/alpha-centauri-harald-lesch-videothek-ID1207836664586.xml. Last accessed 13. May 2010. A web-page with German video-documentations delivered a lot of vivid examples about chaos for my poster. Poster Section 2: Laplace's Demon and the Butterfly Effect In this part I describe the idea of the so called Laplace's Demon and the theory of cause-and-effect chains. I work with a lot of examples, especially the famous weather forecast example. Also too I introduce the mathematical concept of a dynamic system. Jeremy S. Heyl (August 11, 2008). The Double Pendulum Fractal. British Columbia, Canada. -
Instructional Experiments on Nonlinear Dynamics & Chaos (And
Bibliography of instructional experiments on nonlinear dynamics and chaos Page 1 of 20 Colorado Virtual Campus of Physics Mechanics & Nonlinear Dynamics Cluster Nonlinear Dynamics & Chaos Lab Instructional Experiments on Nonlinear Dynamics & Chaos (and some related theory papers) overviews of nonlinear & chaotic dynamics prototypical nonlinear equations and their simulation analysis of data from chaotic systems control of chaos fractals solitons chaos in Hamiltonian/nondissipative systems & Lagrangian chaos in fluid flow quantum chaos nonlinear oscillators, vibrations & strings chaotic electronic circuits coupled systems, mode interaction & synchronization bouncing ball, dripping faucet, kicked rotor & other discrete interval dynamics nonlinear dynamics of the pendulum inverted pendulum swinging Atwood's machine pumping a swing parametric instability instabilities, bifurcations & catastrophes chemical and biological oscillators & reaction/diffusions systems other pattern forming systems & self-organized criticality miscellaneous nonlinear & chaotic systems -overviews of nonlinear & chaotic dynamics To top? Briggs, K. (1987), "Simple experiments in chaotic dynamics," Am. J. Phys. 55 (12), 1083-9. Hilborn, R. C. (2004), "Sea gulls, butterflies, and grasshoppers: a brief history of the butterfly effect in nonlinear dynamics," Am. J. Phys. 72 (4), 425-7. Hilborn, R. C. and N. B. Tufillaro (1997), "Resource Letter: ND-1: nonlinear dynamics," Am. J. Phys. 65 (9), 822-34. Laws, P. W. (2004), "A unit on oscillations, determinism and chaos for introductory physics students," Am. J. Phys. 72 (4), 446-52. Sungar, N., J. P. Sharpe, M. J. Moelter, N. Fleishon, K. Morrison, J. McDill, and R. Schoonover (2001), "A laboratory-based nonlinear dynamics course for science and engineering students," Am. J. Phys. 69 (5), 591-7. http://carbon.cudenver.edu/~rtagg/CVCP/Ctr_dynamics/Lab_nonlinear_dyn/Bibex_nonline.. -
Summary of Unit 3 Chaos and the Butterfly Effect
Summary of Unit 3 Chaos and the Butterfly Effect Introduction to Dynamical Systems and Chaos http://www.complexityexplorer.org The Logistic Equation ● A very simple model of a population where there is some limit to growth ● f(x) = rx(1-x) ● r is a growth parameter ● x is measured as a fraction of the “annihilation” parameter. ● f(x) gives the population next year given x, the population this year. David P. Feldman Introduction to Dynamical Systems http://www.complexityexplorer.org and Chaos Iterating the Logistic Equation ● We used an online program to iterate the logistic equation for different r values and make time series plots ● We found attracting periodic behavior of different periods and... David P. Feldman Introduction to Dynamical Systems http://www.complexityexplorer.org and Chaos Aperiodic Orbits ● For r=4 (and other values), the orbit is aperiodic. It never repeats. ● Applying the same function over and over again does not result in periodic behavior. David P. Feldman Introduction to Dynamical Systems http://www.complexityexplorer.org and Chaos Comparing Initial Conditions ● We used a different online program to compare time series for two different initial conditions. ● The bottom plot is the difference between the two time series in the top plot. David P. Feldman Introduction to Dynamical Systems http://www.complexityexplorer.org and Chaos Sensitive Dependence on Initial Conditions ● When r=4.0, two orbits that start very close together eventually end up far apart ● This is known as sensitive dependence on initial conditions, or the butterfly effect. David P. Feldman Introduction to Dynamical Systems http://www.complexityexplorer.org and Chaos Sensitive Dependence on Initial Conditions ● For any initial condition x there is another initial condition very near to it that eventually ends up far away ● To predict the behavior of a system with SDIC requires knowing the initial condition with impossible accuracy. -
Symmetric Encryption Algorithms Using Chaotic and Non-Chaotic Generators: a Review
Journal of Advanced Research (2016) 7, 193–208 Cairo University Journal of Advanced Research REVIEW Symmetric encryption algorithms using chaotic and non-chaotic generators: A review Ahmed G. Radwan a,b,*, Sherif H. AbdElHaleem a, Salwa K. Abd-El-Hafiz a a Engineering Mathematics Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt b Nanoelectronics Integrated Systems Center (NISC), Nile University, Cairo, Egypt GRAPHICAL ABSTRACT ARTICLE INFO ABSTRACT Article history: This paper summarizes the symmetric image encryption results of 27 different algorithms, which Received 27 May 2015 include substitution-only, permutation-only or both phases. The cores of these algorithms are Received in revised form 24 July 2015 based on several discrete chaotic maps (Arnold’s cat map and a combination of three general- Accepted 27 July 2015 ized maps), one continuous chaotic system (Lorenz) and two non-chaotic generators (fractals Available online 1 August 2015 and chess-based algorithms). Each algorithm has been analyzed by the correlation coefficients * Corresponding author. Tel.: +20 1224647440; fax: +20 235723486. E-mail address: [email protected] (A.G. Radwan). Peer review under responsibility of Cairo University. Production and hosting by Elsevier http://dx.doi.org/10.1016/j.jare.2015.07.002 2090-1232 ª 2015 Production and hosting by Elsevier B.V. on behalf of Cairo University. 194 A.G. Radwan et al. between pixels (horizontal, vertical and diagonal), differential attack measures, Mean Square Keywords: Error (MSE), entropy, sensitivity analyses and the 15 standard tests of the National Institute Permutation matrix of Standards and Technology (NIST) SP-800-22 statistical suite. The analyzed algorithms Symmetric encryption include a set of new image encryption algorithms based on non-chaotic generators, either using Chess substitution only (using fractals) and permutation only (chess-based) or both. -
Chaos: the Mathematics Behind the Butterfly Effect
Chaos: The Mathematics Behind the Butterfly E↵ect James Manning Advisor: Jan Holly Colby College Mathematics Spring, 2017 1 1. Introduction A butterfly flaps its wings, and a hurricane hits somewhere many miles away. Can these two events possibly be related? This is an adage known to many but understood by few. That fact is based on the difficulty of the mathematics behind the adage. Now, it must be stated that, in fact, the flapping of a butterfly’s wings is not actually known to be the reason for any natural disasters, but the idea of it does get at the driving force of Chaos Theory. The common theme among the two is sensitive dependence on initial conditions. This is an idea that will be revisited later in the paper, because we must first cover the concepts necessary to frame chaos. This paper will explore one, two, and three dimensional systems, maps, bifurcations, limit cycles, attractors, and strange attractors before looking into the mechanics of chaos. Once chaos is introduced, we will look in depth at the Lorenz Equations. 2. One Dimensional Systems We begin our study by looking at nonlinear systems in one dimen- sion. One of the important features of these is the nonlinearity. Non- linearity in an equation evokes behavior that is not easily predicted due to the disproportionate nature of inputs and outputs. Also, the term “system”isoftenamisnomerbecauseitoftenevokestheideaof asystemofequations.Thiswillbethecaseaswemoveourfocuso↵ of one dimension, but for now we do not want to think of a system of equations. In this case, the type of system we want to consider is a first-order system of a single equation. -
The Lorenz System
The Lorenz system James Hateley Contents 1 Formulation 2 2 Fixed points 4 3 Attractors 4 4 Phase analysis 5 4.1 Local Stability at The Origin....................................5 4.2 Global Stability............................................6 4.2.1 0 < ρ < 1...........................................6 4.2.2 1 < ρ < ρh ..........................................6 4.3 Bifurcations..............................................6 4.3.1 Supercritical pitchfork bifurcation: ρ = 1.........................7 4.3.2 Subcritical Hopf-Bifurcation: ρ = ρh ............................8 4.4 Strange Attracting Sets.......................................8 4.4.1 Homoclinic Orbits......................................9 4.4.2 Poincar´eMap.........................................9 4.4.3 Dynamics at ρ∗ ........................................9 4.4.4 Asymptotic Behavior..................................... 10 4.4.5 A Few Remarks........................................ 10 5 Numerical Simulations and Figures 10 5.1 Globally stable , 0 < ρ < 1...................................... 10 5.2 Pitchfork Bifurcation, ρ = 1..................................... 12 5.3 Homoclinic orbit........................................... 12 5.4 Transient Chaos........................................... 12 5.5 Chaos................................................. 14 5.5.1 A plot for ρh ......................................... 14 5.6 Other Notable Orbits........................................ 16 5.6.1 Double Periodic Orbits................................... 16 5.6.2 Intermittent -
SPATIOTEMPORAL CHAOS in COUPLED MAP LATTICE Itishree
SPATIOTEMPORAL CHAOS IN COUPLED MAP LATTICE By Itishree Priyadarshini Under the Guidance of Prof. Biplab Ganguli Department of Physics National Institute of Technology, Rourkela CERTIFICATE This is to certify that the project thesis entitled ” Spatiotemporal chaos in Cou- pled Map Lattice ” being submitted by Itishree Priyadarshini in partial fulfilment to the requirement of the one year project course (PH 592) of MSc Degree in physics of National Institute of Technology, Rourkela has been carried out under my super- vision. The result incorporated in the thesis has been produced by developing her own computer codes. Prof. Biplab Ganguli Dept. of Physics National Institute of Technology Rourkela - 769008 1 ACKNOWLEDGEMENT I would like to acknowledge my guide Prof. Biplab Ganguli for his help and guidance in the completion of my one-year project and also for his enormous moti- vation and encouragement. I am also very much thankful to research scholars whose encouragement and support helped me to complete my project. 2 ABSTRACT The sensitive dependence on initial condition, which is the essential feature of chaos is demonstrated through simple Lorenz model. Period doubling route to chaos is shown by analysis of Logistic map and other different route to chaos is discussed. Coupled map lattices are investigated as a model for spatio-temporal chaos. Diffusively coupled logistic lattice is studied which shows different pattern in accordance with the coupling constant and the non-linear parameter i.e. frozen random pattern, pattern selection with suppression of chaos , Brownian motion of the space defect, intermittent collapse, soliton turbulence and travelling waves. 3 Contents 1 Introduction 3 2 Chaos 3 3 Lorenz System 4 4 Route to Chaos 6 4.1 PeriodDoubling............................. -
Unstable Dimension Variability Structure in the Parameter Space of Coupled Hénon Maps
Applied Mathematics and Computation 286 (2016) 23–28 Contents lists available at ScienceDirect Applied Mathematics and Computation journal homepage: www.elsevier.com/locate/amc Unstable dimension variability structure in the parameter space of coupled Hénon maps ∗ Vagner dos Santos a, José D. Szezech Jr. a,b, , Murilo S. Baptista c, Antonio M. Batista a,b,d, Iberê L. Caldas d a Pós-Graduação em Ciências, Universidade Estadual de Ponta Grossa, 84030-900 Ponta Grossa, PR, Brazil b Departamento de Matemática e Estatística, Universidade Estadual de Ponta Grossa, 84030-900 Ponta Grossa, PR, Brazil c Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, AB24 3UE Aberdeen, UK d Instituto de Física, Universidade de São Paulo, 05508-090 São Paulo, SP, Brazil a r t i c l e i n f o a b s t r a c t Keywords: Coupled maps have been investigated to model the applications of periodic and chaotic UDV dynamics of spatially extended systems. We have studied the parameter space of coupled Shrimp Hénon maps and showed that attractors possessing unstable dimension variability (UDV) Hénon map appear for parameters neighbouring the so called shrimp domains, representing param- Parameter-space Lyapunov exponents eters leading to stable periodic behaviour. Therefore, the UDV should be very likely to be found in the same large class of natural and man-made systems that present shrimp domains. ©2016 Elsevier Inc. All rights reserved. 1. Introduction In the early 1960s, Lorenz proposed three coupled nonlinear differential equations to model atmospheric convection [1] . The Lorenz equations have attracted great attention due to their interesting dynamical solutions, for instance, a chaotic at- tractor [2,3] .