Appendix. Ergodicity of the Geodesic Flow
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Chapter 5. Flow Maps and Dynamical Systems
Chapter 5 Flow Maps and Dynamical Systems Main concepts: In this chapter we introduce the concepts of continuous and discrete dynamical systems on phase space. Keywords are: classical mechanics, phase space, vector field, linear systems, flow maps, dynamical systems Figure 5.1: A solar system is well modelled by classical mechanics. (source: Wikimedia Commons) In Chapter 1 we encountered a variety of ODEs in one or several variables. Only in a few cases could we write out the solution in a simple closed form. In most cases, the solutions will only be obtainable in some approximate sense, either from an asymptotic expansion or a numerical computation. Yet, as discussed in Chapter 3, many smooth systems of differential equations are known to have solutions, even globally defined ones, and so in principle we can suppose the existence of a trajectory through each point of phase space (or through “almost all” initial points). For such systems we will use the existence of such a solution to define a map called the flow map that takes points forward t units in time from their initial points. The concept of the flow map is very helpful in exploring the qualitative behavior of numerical methods, since it is often possible to think of numerical methods as approximations of the flow map and to evaluate methods by comparing the properties of the map associated to the numerical scheme to those of the flow map. 25 26 CHAPTER 5. FLOW MAPS AND DYNAMICAL SYSTEMS In this chapter we will address autonomous ODEs (recall 1.13) only: 0 d y = f(y), y, f ∈ R (5.1) 5.1 Classical mechanics In Chapter 1 we introduced models from population dynamics. -
Flows of Continuous-Time Dynamical Systems with No Periodic Orbit As an Equivalence Class Under Topological Conjugacy Relation
Journal of Mathematics and Statistics 7 (3): 207-215, 2011 ISSN 1549-3644 © 2011 Science Publications Flows of Continuous-Time Dynamical Systems with No Periodic Orbit as an Equivalence Class under Topological Conjugacy Relation Tahir Ahmad and Tan Lit Ken Department of Mathematics, Faculty of Science, Nanotechnology Research Alliance, Theoretical and Computational Modeling for Complex Systems, University Technology Malaysia 81310 Skudai, Johor, Malaysia Abstract: Problem statement: Flows of continuous-time dynamical systems with the same number of equilibrium points and trajectories, and which has no periodic orbit form an equivalence class under the topological conjugacy relation. Approach: Arbitrarily, two trajectories resulting from two distinct flows of this type of dynamical systems were written as a set of points (orbit). A homeomorphism which maps between these two sets is then built. Using the notion of topological conjugacy, they were shown to conjugate topologically. By the arbitrariness in selection of flows and their respective initial states, the results were extended to all the flows of dynamical system of that type. Results: Any two flows of such dynamical systems were shown to share the same dynamics temporally along with other properties such as order isomorphic and homeomorphic. Conclusion: Topological conjugacy serves as an equivalence relation in the set of flows of continuous-time dynamical systems which have same number of equilibrium points and trajectories, and has no periodic orbit. Key words: Dynamical system, equilibrium points, trajectories, periodic orbit, equivalence class, topological conjugacy, order isomorphic, Flat Electroencephalography (Flat EEG), dynamical systems INTRODUCTION models. Nandhakumar et al . (2009) for example, the dynamics of robot arm is studied. -
Dynamical Systems Theory
Dynamical Systems Theory Bjorn¨ Birnir Center for Complex and Nonlinear Dynamics and Department of Mathematics University of California Santa Barbara1 1 c 2008, Bjorn¨ Birnir. All rights reserved. 2 Contents 1 Introduction 9 1.1 The 3 Body Problem . .9 1.2 Nonlinear Dynamical Systems Theory . 11 1.3 The Nonlinear Pendulum . 11 1.4 The Homoclinic Tangle . 18 2 Existence, Uniqueness and Invariance 25 2.1 The Picard Existence Theorem . 25 2.2 Global Solutions . 35 2.3 Lyapunov Stability . 39 2.4 Absorbing Sets, Omega-Limit Sets and Attractors . 42 3 The Geometry of Flows 51 3.1 Vector Fields and Flows . 51 3.2 The Tangent Space . 58 3.3 Flow Equivalance . 60 4 Invariant Manifolds 65 5 Chaotic Dynamics 75 5.1 Maps and Diffeomorphisms . 75 5.2 Classification of Flows and Maps . 81 5.3 Horseshoe Maps and Symbolic Dynamics . 84 5.4 The Smale-Birkhoff Homoclinic Theorem . 95 5.5 The Melnikov Method . 96 5.6 Transient Dynamics . 99 6 Center Manifolds 103 3 4 CONTENTS 7 Bifurcation Theory 109 7.1 Codimension One Bifurcations . 110 7.1.1 The Saddle-Node Bifurcation . 111 7.1.2 A Transcritical Bifurcation . 113 7.1.3 A Pitchfork Bifurcation . 115 7.2 The Poincare´ Map . 118 7.3 The Period Doubling Bifurcation . 119 7.4 The Hopf Bifurcation . 121 8 The Period Doubling Cascade 123 8.1 The Quadradic Map . 123 8.2 Scaling Behaviour . 130 8.2.1 The Singularly Supported Strange Attractor . 138 A The Homoclinic Orbits of the Pendulum 141 List of Figures 1.1 The rotation of the Sun and Jupiter in a plane around a common center of mass and the motion of a comet perpendicular to the plane. -
STABLE ERGODICITY 1. Introduction a Dynamical System Is Ergodic If It
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 41, Number 1, Pages 1{41 S 0273-0979(03)00998-4 Article electronically published on November 4, 2003 STABLE ERGODICITY CHARLES PUGH, MICHAEL SHUB, AND AN APPENDIX BY ALEXANDER STARKOV 1. Introduction A dynamical system is ergodic if it preserves a measure and each measurable invariant set is a zero set or the complement of a zero set. No measurable invariant set has intermediate measure. See also Section 6. The classic real world example of ergodicity is how gas particles mix. At time zero, chambers of oxygen and nitrogen are separated by a wall. When the wall is removed, the gasses mix thoroughly as time tends to infinity. In contrast think of the rotation of a sphere. All points move along latitudes, and ergodicity fails due to existence of invariant equatorial bands. Ergodicity is stable if it persists under perturbation of the dynamical system. In this paper we ask: \How common are ergodicity and stable ergodicity?" and we propose an answer along the lines of the Boltzmann hypothesis { \very." There are two competing forces that govern ergodicity { hyperbolicity and the Kolmogorov-Arnold-Moser (KAM) phenomenon. The former promotes ergodicity and the latter impedes it. One of the striking applications of KAM theory and its more recent variants is the existence of open sets of volume preserving dynamical systems, each of which possesses a positive measure set of invariant tori and hence fails to be ergodic. Stable ergodicity fails dramatically for these systems. But does the lack of ergodicity persist if the system is weakly coupled to another? That is, what happens if you have a KAM system or one of its perturbations that refuses to be ergodic, due to these positive measure sets of invariant tori, but somewhere in the universe there is a hyperbolic or partially hyperbolic system weakly coupled to it? Does the lack of egrodicity persist? The answer is \no," at least under reasonable conditions on the hyperbolic factor. -
Von Neumann Algebras
Proceedings of the International Congress of Mathematicians Helsinki, 1978 von Neumann Algebras A. Connes For every selfadjoint operator T in the Hilbert space H,1 f(T) makes sense not only in the obvious case where / is a polynomial but also if / is just measurable, and if fn(x)-+f(x) for all x£R (with (/,) bounded) then fn(T)-+f(T) weakly, i.e. <fn(T)£9i)+<f(T)£9ti)V£9fiCH. Moreover the set {f(T)9 f measurable} is the set of all operators S in H invariant under all unitary transformations of H which fix T. More generally, if (Tf), i=\, ..., k, are operators in H then the weak closure of the set of polynomials in Ti9 T* is the space of all operators in H invariant under all the unitaries fixing the Ti9 as follows from the bicommutation theorem of von Neumann (1929): A subset M of L(H) is the commutant of a subgroup G of the unitary group U(H) iff it is a weakly closed * subalgebra of L(H) (containing the identity \). Such an algebra is called a von Neumann algebra (or ring of operators). Any commutative one is of the form {f(T),f measurable} for a selfadjoint T, and hence is the algebra of essentially bounded measurable functions : L°° (Spectrum T, Spectral measure T). In general the center of M is a commutative von Neumann algebra and hence an L°°(X,p) for some measure space X, then M={(T(x))xex, T(x)£M(x)\/x£X} is the algebra of all essentially bounded measurable sections of a family M(x), x£X, of von Neumann algebras with trivial centers, i.e. -
THE DYNAMICAL SYSTEMS APPROACH to DIFFERENTIAL EQUATIONS INTRODUCTION the Mathematical Subject We Call Dynamical Systems Was
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 11, Number 1, July 1984 THE DYNAMICAL SYSTEMS APPROACH TO DIFFERENTIAL EQUATIONS BY MORRIS W. HIRSCH1 This harmony that human intelligence believes it discovers in nature —does it exist apart from that intelligence? No, without doubt, a reality completely independent of the spirit which conceives it, sees it or feels it, is an impossibility. A world so exterior as that, even if it existed, would be forever inaccessible to us. But what we call objective reality is, in the last analysis, that which is common to several thinking beings, and could be common to all; this common part, we will see, can be nothing but the harmony expressed by mathematical laws. H. Poincaré, La valeur de la science, p. 9 ... ignorance of the roots of the subject has its price—no one denies that modern formulations are clear, elegant and precise; it's just that it's impossible to comprehend how any one ever thought of them. M. Spivak, A comprehensive introduction to differential geometry INTRODUCTION The mathematical subject we call dynamical systems was fathered by Poin caré, developed sturdily under Birkhoff, and has enjoyed a vigorous new growth for the last twenty years. As I try to show in Chapter I, it is interesting to look at this mathematical development as the natural outcome of a much broader theme which is as old as science itself: the universe is a sytem which changes in time. Every mathematician has a particular way of thinking about mathematics, but rarely makes it explicit. -
Vector Fields and Dynamical Systems
Jim Lambers MAT 605 Fall Semester 2015-16 Lecture 3 Notes These notes correspond to Section 1.2 in the text. Vector Fields and Dynamical Systems Since a dynamical system prescribes the tangent vector of the solution, it is natural to identify a dynamical system with a vector field, that expresses the tangent vector as a function of the independent and dependent variables of the system. This facilitates the study of the system and its solutions from a geometric perspective. n n Definition 1 (Vector Field) A vector field is a function X : D ⊆ R ! R . For each x 2 D, we write X(x) = X1(x);X2(x);:::;Xn(x) : The functions Xi, i = 1; 2; : : : ; n, are called the component functions of X. 3 4 Example 1 The vector field X(x1; x2) = (x1x2; x1 + x1x2) corresponds to the dynamical system 0 x1 = x1x2 0 3 4 x2 = x1 + x1x2: 1 2 3 4 The component functions of X are X (x1; x2) = x1x2 and X (x1; x2) = x1 + x1x2. 2 Now, we can state a more precise definition of a solution of a dynamical system. n Definition 2 (Solutions of Autonomous Systems) A curve in R is a function n r : I ! R , where I ⊆ R is an interval. If r is differentiable, we say that r is a differentiable curve. n n Let X : D ⊆ R ! R be a vector field. A solution of the autonomous dynami- cal system x0 = X(x); also known as a solution curve, integral curve (of X) or streamline, is a differen- tiable curve r : I ! D such that r0(t) = X(r(t)); t 2 I: Vector fields and their associated integral curves have physical interpretations in various appli- cations. -
An Introduction to Infinite Ergodic Theory, 1997 49 R
http://dx.doi.org/10.1090/surv/050 Selected Titles in This Series 50 Jon Aaronson, An introduction to infinite ergodic theory, 1997 49 R. E. Showalter, Monotone operators in Banach space and nonlinear partial differential equations, 1997 48 Paul-Jean Cahen and Jean-Luc Chabert, Integer-valued polynomials, 1997 47 A. D. Elmendorf, I. Kriz, M. A. Mandell, and J. P. May (with an appendix by M. Cole), Rings, modules, and algebras in stable homotopy theory, 1997 46 Stephen Lipscomb, Symmetric inverse semigroups, 1996 45 George M. Bergman and Adam O. Hausknecht, Cogroups and co-rings in categories of associative rings, 1996 44 J. Amoros, M. Burger, K. Corlette, D. Kotschick, and D. Toledo, Fundamental groups of compact Kahler manifolds, 1996 43 James E. Humphreys, Conjugacy classes in semisimple algebraic groups, 1995 42 Ralph Preese, Jaroslav Jezek, and J. B. Nation, Free lattices, 1995 41 Hal L. Smith, Monotone dynamical systems: an introduction to the theory of competitive and cooperative systems, 1995 40.2 Daniel Gorenstein, Richard Lyons, and Ronald Solomon, The classification of the finite simple groups, number 2, 1995 40.1 Daniel Gorenstein, Richard Lyons, and Ronald Solomon, The classification of the finite simple groups, number 1, 1994 39 Sigurdur Helgason, Geometric analysis on symmetric spaces, 1994 38 Guy David and Stephen Semmes, Analysis of and on uniformly rectifiable sets, 1993 37 Leonard Lewin, Editor, Structural properties of polylogarithms, 1991 36 John B. Conway, The theory of subnormal operators, 1991 35 Shreeram S. Abhyankar, Algebraic geometry for scientists and engineers, 1990 34 Victor Isakov, Inverse source problems, 1990 33 Vladimir G. -
Lecture 3: Dynamical Systems 2
15-382 COLLECTIVE INTELLIGENCE – S19 LECTURE 3: DYNAMICAL SYSTEMS 2 TEACHER: GIANNI A. DI CARO GENERAL DEFINITION OF DYNAMICAL SYSTEMS A dynamical system is a 3-tuple ", !, Φ : § ! is a set of all possible states of the dynamical system (the state space) § " is the set of values the time (evolution) parameter can take § Φ is the evolution function of the dynamical system, that associates to each $ ∈ ! a unique image in ! depending on the time parameter &, (not all pairs (&, $) are feasible, that requires introducing the subset *) Φ: * ⊆ "×! → ! Ø Φ 0, $ = $1 (the initial condition) Ø Φ &2, 3 &4, $ = Φ(&2 + &4, $), (property of states) for &4, &4+&2 ∈ 6($), &2 ∈ 6(Φ(&4$)), 6 $ = {& ∈ " ∶ (&, $) ∈ *} Ø The evolution function Φ provides the system state (the value) at time & for any initial state $1 Ø :; = {Φ &, $ ∶ & ∈ 6 $ } orbit (flow lines) of the system through $, starting in $ , the set of visited states as a function of time: $(&) 2 TYPES OF DYNAMICAL SYSTEMS § Informally: A dynamical system defines a deterministic rule that allows to know the current state as a function of past states § Given an initial condition !" = !(0) ∈ (, a deterministic trajectory ! ) , ) ∈ + !" , is produced by ,, (, Φ § States can be “anything” mathematically well-behaved that represent situations of interest § The nature of the set , and of the function Φ give raise to different classes of dynamical systems (and resulting properties and trajectories) 3 TYPES OF DYNAMICAL SYSTEMS § Continuous time dynamical systems (Flows): ! open interval of ℝ, Φ continous and differentiable function à Differential equations § Φ represents a flow, defining a smooth (differentiable) continuous curve § The notion of flow builds on and formalizes the idea of the motion of particles in a fluid: it can be viewed as the abstract representation of (continuous) motion of points over time. -
A Dynamical Systems Theory of Thermodynamics
© Copyright, Princeton University Press. No part of this book may be distributed, posted, or reproduced in any form by digital or mechanical means without prior written permission of the publisher. Chapter One Introduction 1.1 An Overview of Classical Thermodynamics Energy is a concept that underlies our understanding of all physical phenomena and is a measure of the ability of a dynamical system to produce changes (motion) in its own system state as well as changes in the system states of its surroundings. Thermodynamics is a physical branch of science that deals with laws governing energy flow from one body to another and energy transformations from one form to another. These energy flow laws are captured by the fundamental principles known as the first and second laws of thermodynamics. The first law of thermodynamics gives a precise formulation of the equivalence between heat (i.e., the transferring of energy via temperature gradients) and work (i.e., the transferring of energy into coherent motion) and states that, among all system transformations, the net system energy is conserved. Hence, energy cannot be created out of nothing and cannot be destroyed; it can merely be transferred from one form to another. The law of conservation of energy is not a mathematical truth, but rather the consequence of an immeasurable culmination of observations over the chronicle of our civilization and is a fundamental axiom of the science of heat. The first law does not tell us whether any particular process can actually occur, that is, it does not restrict the ability to convert work into heat or heat into work, except that energy must be conserved in the process. -
Are Hamiltonian Flows Geodesic Flows?
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 355, Number 3, Pages 1237{1250 S 0002-9947(02)03167-7 Article electronically published on October 17, 2002 ARE HAMILTONIAN FLOWS GEODESIC FLOWS? CHRISTOPHER MCCORD, KENNETH R. MEYER, AND DANIEL OFFIN Abstract. When a Hamiltonian system has a \Kinetic + Potential" struc- ture, the resulting flow is locally a geodesic flow. But there may be singularities of the geodesic structure; so the local structure does not always imply that the flow is globally a geodesic flow. In order for a flow to be a geodesic flow, the underlying manifold must have the structure of a unit tangent bundle. We develop homological conditions for a manifold to have such a structure. We apply these criteria to several classical examples: a particle in a po- tential well, the double spherical pendulum, the Kovalevskaya top, and the N-body problem. We show that the flow of the reduced planar N-body prob- lem and the reduced spatial 3-body are never geodesic flows except when the angular momentum is zero and the energy is positive. 1. Introduction Geodesic flows are always Hamiltonian. That is, given a Riemannian manifold M,themetricG can be considered as a function on either the tangent bundle or the cotangent bundle T ∗M. The cotangent bundle has a natural symplectic structure, and so the function G considered as a Hamiltonian defines a Hamiltonian flow on T ∗M. This flow is the same as the geodesic flow defined by the metric G when it is transferred to the cotangent bundle [1]. In this paper we begin the study of the inverse problem [10] and ask when is a Hamiltonian flow a geodesic flow, or more generally a reparameterization of a geodesic flow. -
The Phase Flow Method
Journal of Computational Physics 220 (2006) 184–215 www.elsevier.com/locate/jcp The phase flow method Lexing Ying *, Emmanuel J. Cande`s Applied and Computational Mathematics, California Institute of Technology, Caltech, MC 217-50, Pasadena, CA 91125, United States Received 4 October 2005; received in revised form 6 March 2006; accepted 5 May 2006 Available online 27 June 2006 Abstract This paper introduces the phase flow method, a novel, accurate and fast approach for constructing phase maps for non- linear autonomous ordinary differential equations. The method operates by initially constructing the phase map for small times using a standard ODE integration rule and builds up the phase map for larger times with the help of a local inter- polation scheme together with the group property of the phase flow. The computational complexity of building up the complete phase map is usually that of tracing a few rays. In addition, the phase flow method is provably and empirically very accurate. Once the phase map is available, integrating the ODE for initial conditions on the invariant manifold only makes use of local interpolation, thus having constant complexity. The paper develops applications in the field of high frequency wave propagation, and shows how to use the phase flow method to (1) rapidly propagate wave fronts, (2) rapidly calculate wave amplitudes along these wave fronts, and (3) rapidly evaluate multiple wave arrival times at arbitrary locations. Ó 2006 Elsevier Inc. All rights reserved. Keywords: The phase flow method; Ordinary differential equations; Phase maps; Phase flow; Interpolation; High-frequency wave prop- agation; Geometrical optics; Hamiltonian dynamics; Ray equations; Wave arrival times; Eulerian and Lagrangian formulations 1.