AMS Short Course on Rigorous Numerics in Dynamics: (Un)Stable Manifolds and Connecting Orbits
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Approximating Stable and Unstable Manifolds in Experiments
PHYSICAL REVIEW E 67, 037201 ͑2003͒ Approximating stable and unstable manifolds in experiments Ioana Triandaf,1 Erik M. Bollt,2 and Ira B. Schwartz1 1Code 6792, Plasma Physics Division, Naval Research Laboratory, Washington, DC 20375 2Department of Mathematics and Computer Science, Clarkson University, P.O. Box 5815, Potsdam, New York 13699 ͑Received 5 August 2002; revised manuscript received 23 December 2002; published 12 March 2003͒ We introduce a procedure to reveal invariant stable and unstable manifolds, given only experimental data. We assume a model is not available and show how coordinate delay embedding coupled with invariant phase space regions can be used to construct stable and unstable manifolds of an embedded saddle. We show that the method is able to capture the fine structure of the manifold, is independent of dimension, and is efficient relative to previous techniques. DOI: 10.1103/PhysRevE.67.037201 PACS number͑s͒: 05.45.Ac, 42.60.Mi Many nonlinear phenomena can be explained by under- We consider a basin saddle of Eq. ͑1͒ which lies on the standing the behavior of the unstable dynamical objects basin boundary between a chaotic attractor and a period-four present in the dynamics. Dynamical mechanisms underlying periodic attractor. The chosen parameters are in a region in chaos may be described by examining the stable and unstable which the chaotic attractor disappears and only a periodic invariant manifolds corresponding to unstable objects, such attractor persists along with chaotic transients due to inter- as saddles ͓1͔. Applications of the manifold topology have secting stable and unstable manifolds of the basin saddle. -
Robust Transitivity of Singular Hyperbolic Attractors
Robust transitivity of singular hyperbolic attractors Sylvain Crovisier∗ Dawei Yangy January 22, 2020 Abstract Singular hyperbolicity is a weakened form of hyperbolicity that has been introduced for vector fields in order to allow non-isolated singularities inside the non-wandering set. A typical example of a singular hyperbolic set is the Lorenz attractor. However, in contrast to uniform hyperbolicity, singular hyperbolicity does not immediately imply robust topological properties, such as the transitivity. In this paper, we prove that open and densely inside the space of C1 vector fields of a compact manifold, any singular hyperbolic attractors is robustly transitive. 1 Introduction Lorenz [L] in 1963 studied some polynomial ordinary differential equations in R3. He found some strange attractor with the help of computers. By trying to understand the chaotic dynamics in Lorenz' systems, [ABS, G, GW] constructed some geometric abstract models which are called geometrical Lorenz attractors: these are robustly transitive non- hyperbolic chaotic attractors with singularities in three-dimensional manifolds. In order to study attractors containing singularities for general vector fields, Morales- Pacifico-Pujals [MPP] first gave the notion of singular hyperbolicity in dimension 3. This notion can be adapted to the higher dimensional case, see [BdL, CdLYZ, MM, ZGW]. In the absence of singularity, the singular hyperbolicity coincides with the usual notion of uniform hyperbolicity; in that case it has many nice dynamical consequences: spectral decomposition, stability, probabilistic description,... But there also exist open classes of vector fields exhibiting singular hyperbolic attractors with singularity: the geometrical Lorenz attractors are such examples. In order to have a description of the dynamics of arXiv:2001.07293v1 [math.DS] 21 Jan 2020 general flows, we thus need to develop a systematic study of the singular hyperbolicity in the presence of singularity. -
A Negative Result for Hearing the Shape of a Triangle: a Computer-Assisted Proof
AN ELECTRONIC JOURNAL OF THE SOCIETAT CATALANA DE MATEMATIQUES` A negative result for hearing the shape of a triangle: a computer-assisted proof ⇤Gerard Orriols Gim´enez Resum (CAT) ETH Z¨urich. En aquest article demostrem que existeixen dos triangles diferents pels quals [email protected] el primer, segon i quart valor propi del Laplaci`a amb condicions de Dirichlet coincideixen. Aix`o resol una conjectura proposada per Antunes i Freitas i Corresponding author ⇤ suggerida per la seva evid`encia num`erica. La prova ´esassistida per ordinador i utilitza una nova t`ecnica per tractar l’espectre d’un l’operador, que consisteix a combinar un M`etode d’Elements Finits per localitzar aproximadament els primers valors propis i controlar la seva posici´oa l’espectre, juntament amb el M`etode de Solucions Particulars per confinar aquests valors propis a un interval molt m´esprec´ıs. Abstract (ENG) We prove that there exist two distinct triangles for which the first, second and fourth eigenvalues of the Laplace operator with zero Dirichlet boundary conditions coincide. This solves a conjecture raised by Antunes and Freitas and suggested by their numerical evidence. We use a novel technique for a computer-assisted proof about the spectrum of an operator, which combines a Finite Element Method, to locate roughly the first eigenvalues keeping track of their position in the spectrum, and the Method of Particular Solutions, to get a much more precise bound of these eigenvalues. Keywords: computer-assisted proof, Laplace eigenvalues, spectral geome- Acknowledgement try, Finite Element Method, Method The author would like to thank Re- of Particular Solutions. -
Hartman-Grobman and Stable Manifold Theorem
THE HARTMAN-GROBMAN AND STABLE MANIFOLD THEOREM SLOBODAN N. SIMIC´ This is a summary of some basic results in dynamical systems we discussed in class. Recall that there are two kinds of dynamical systems: discrete and continuous. A discrete dynamical system is map f : M → M of some space M.A continuous dynamical system (or flow) is a collection of maps φt : M → M, with t ∈ R, satisfying φ0(x) = x and φs+t(x) = φs(φt(x)), for all x ∈ M and s, t ∈ R. We call M the phase space. It is usually either a subset of a Euclidean space, a metric space or a smooth manifold (think of surfaces in 3-space). Similarly, f or φt are usually nice maps, e.g., continuous or differentiable. We will focus on smooth (i.e., differentiable any number of times) discrete dynamical systems. Let f : M → M be one such system. Definition 1. The positive or forward orbit of p ∈ M is the set 2 O+(p) = {p, f(p), f (p),...}. If f is invertible, we define the (full) orbit of p by k O(p) = {f (p): k ∈ Z}. We can interpret a point p ∈ M as the state of some physical system at time t = 0 and f k(p) as its state at time t = k. The phase space M is the set of all possible states of the system. The goal of the theory of dynamical systems is to understand the long-term behavior of “most” orbits. That is, what happens to f k(p), as k → ∞, for most initial conditions p ∈ M? The first step towards this goal is to understand orbits which have the simplest possible behavior, namely fixed and periodic ones. -
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. -
1 Introduction
数理解析研究所講究録 1169 巻 2000 年 27-56 27 Numerical Verification Methods for Solutions of Ordinary and Partial Differential Equations Mitsuhiro T. Nakao 中尾 充宏 Graduate School of Mathematics, Kyushu University 33 Fukuoka 812-8581, Japan $\mathrm{e}$ -mail: [email protected] Abstract. In this article, we describe on a state of the art of validated numerical computations for solutions of differential equations. A brief overview of the main techniques in self-validating numerics for initial and boundary value problems in ordinary and partial differential equations including eigenvalue problems will be presented. A fairly detailed introductions are given for the author’s own method related to second-order elliptic boundary value problems. Many references which seem to be useful for readers are supplied at the end of the article. Key words. Numerical verification, nonlinear differential equation, computer assisted proof in analysis 1 Introduction If we denote an equation for unknown $u$ by $F(u)=0$ (1.1) then the problem of finding the solution $u$ generally implies an $n$ dimensional simulta- neous system of equations provided $u$ is in some finite dimensional ( $\mathrm{n}$ -dimension) space. Since very large scale, e.g. several thousand, linear system of equations can be easily solved on computers nowadays, it is not too surprising that, even if (1.1) is nonlinear, we can verify the existence and uniqueness of the solution as well as its domain by some numerical computations on the digital computer. However, in the case where (1.1) is a differential equation, it becomes a simultaneous equation which has infinitely many unknowns because the dimension of the potential function space containing $\mathrm{u}$ is infinite. -
Chapter 3 Hyperbolicity
Chapter 3 Hyperbolicity 3.1 The stable manifold theorem In this section we shall mix two typical ideas of hyperbolic dynamical systems: the use of a linear approxi- mation to understand the behavior of nonlinear systems, and the examination of the behavior of the system along a reference orbit. The latter idea is embodied in the notion of stable set. Denition 3.1.1: Let (X, f) be a dynamical system on a metric space X. The stable set of a point x ∈ X is s ∈ k k Wf (x)= y X lim d f (y),f (x) =0 . k→∞ ∈ s In other words, y Wf (x) if and only if the orbit of y is asymptotic to the orbit of x. If no confusion will arise, we drop the index f and write just W s(x) for the stable set of x. Clearly, two stable sets either coincide or are disjoint; therefore X is the disjoint union of its stable sets. Furthermore, f W s(x) W s f(x) for any x ∈ X. The twin notion of unstable set for homeomorphisms is dened in a similar way: Denition 3.1.2: Let f: X → X be a homeomorphism of a metric space X. Then the unstable set of a ∈ point x X is u ∈ k k Wf (x)= y X lim d f (y),f (x) =0 . k→∞ u s In other words, Wf (x)=Wf1(x). Again, unstable sets of homeomorphisms give a partition of the space. On the other hand, stable sets and unstable sets (even of the same point) might intersect, giving rise to interesting dynamical phenomena. -
AMS Short Course on Rigorous Numerics in Dynamics: the Parameterization Method for Stable/Unstable Manifolds of Vector Fields
AMS Short Course on Rigorous Numerics in Dynamics: The Parameterization Method for Stable/Unstable Manifolds of Vector Fields J.D. Mireles James, ∗ February 12, 2017 Abstract This lecture builds on the validated numerical methods for periodic orbits presented in the lecture of J. B. van den Berg. We discuss a functional analytic perspective on validated stability analysis for equilibria and periodic orbits as well as validated com- putation of their local stable/unstable manifolds. Building on this analysis we study heteroclinic and homoclinic connecting orbits between equilibria and periodic orbits of differential equations. We formulate the connecting orbits as solutions of certain projected boundary value problems whcih are amenable to an a posteriori analysis very similar to that already discussed for periodic orbits. The discussion will be driven by several application problems including connecting orbits in the Lorenz system and existence of standing and traveling waves. Contents 1 Introduction 2 2 The Parameterization Method 4 2.1 Formal series solutions for stable/unstable manifolds . .4 2.1.1 Manipulating power series: automatic differentiation and all that . .4 2.1.2 A first example: linearization of an analytic vector field in one complex variable . .9 2.1.3 Stable/unstable manifolds for equilibrium solutions of ordinary differ- ential equations . 15 2.1.4 A more realistic example: stable/unstable manifolds in a restricted four body problem . 18 2.1.5 Stable/unstable manifolds for periodic solutions of ordinary differen- tial equations . 23 2.1.6 Unstable manifolds for equilibrium solutions of partial differential equations . 26 2.1.7 Unstable manifolds for equilibrium and periodic solutions of delay differential equations . -
The Defocusing NLS Equation and Its Normal Form, by B. Grébert and T
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 53, Number 2, April 2016, Pages 337–342 http://dx.doi.org/10.1090/bull/1522 Article electronically published on October 8, 2015 The defocusing NLS equation and its normal form,byB.Gr´ebert and T. Kappeler, EMS Series of Lectures in Mathematics, European Mathematical Society (EMS), Z¨urich, 2014, x+166 pp., ISBN 978-3-03719-131-6, US$38.00 Until the end of the nineteenth century one of the major goals of differential equa- tions theory and mechanics was solving equations of motion, i.e., finding formulae that describe the time dependence of the mechanical configuration. Surprisingly enough, this has been successfully achieved in many interesting cases, such as a planet under the action of gravitation (Kepler problem, two-body problem), the motion of a free rigid body, or the motion of a symmetric rigid body under the action of gravitation (Lagrange top). All these systems are now known to belong to the general class of integrable Hamiltonian systems, for which an explicit procedure of integration of the equations was found by Liouville. Starting from the celebrated paper by Kruskal and Zabusky [ZK65], who dis- covered existence of solitons in the Kortweg–de Vries equation (KdV), mathemati- cians recognized that there are some partial differential equations that are infinite- dimensional analogs of the classical integrable systems. During the last fifty years, the techniques developed for the integration of classical finite-dimensional systems and for the study of their perturbations have been largely adapted to the case of partial differential equations (PDEs). -
Hyperbolic Dynamical Systems, Chaos, and Smale's
Hyperbolic dynamical systems, chaos, and Smale's horseshoe: a guided tour∗ Yuxi Liu Abstract This document gives an illustrated overview of several areas in dynamical systems, at the level of beginning graduate students in mathematics. We start with Poincar´e's discovery of chaos in the three-body problem, and define the Poincar´esection method for studying dynamical systems. Then we discuss the long-term behavior of iterating a n diffeomorphism in R around a fixed point, and obtain the concept of hyperbolicity. As an example, we prove that Arnold's cat map is hyperbolic. Around hyperbolic fixed points, we discover chaotic homoclinic tangles, from which we extract a source of the chaos: Smale's horseshoe. Then we prove that the behavior of the Smale horseshoe is the same as the binary shift, reducing the problem to symbolic dynamics. We conclude with applications to physics. 1 The three-body problem 1.1 A brief history The first thing Newton did, after proposing his law of gravity, is to calculate the orbits of two mass points, M1;M2 moving under the gravity of each other. It is a basic exercise in mechanics to show that, relative to one of the bodies M1, the orbit of M2 is a conic section (line, ellipse, parabola, or hyperbola). The second thing he did was to calculate the orbit of three mass points, in order to study the sun-earth-moon system. Here immediately he encountered difficulty. And indeed, the three body problem is extremely difficult, and the n-body problem is even more so. -
Universal Circles for Quasigeodesic Flows 1 Introduction
Geometry & Topology 10 (2006) 2271–2298 2271 arXiv version: fonts, pagination and layout may vary from GT published version Universal circles for quasigeodesic flows DANNY CALEGARI We show that if M is a hyperbolic 3–manifold which admits a quasigeodesic flow, then π1(M) acts faithfully on a universal circle by homeomorphisms, and preserves a pair of invariant laminations of this circle. As a corollary, we show that the Thurston norm can be characterized by quasigeodesic flows, thereby generalizing a theorem of Mosher, and we give the first example of a closed hyperbolic 3–manifold without a quasigeodesic flow, answering a long-standing question of Thurston. 57R30; 37C10, 37D40, 53C23, 57M50 1 Introduction 1.1 Motivation and background Hyperbolic 3–manifolds can be studied from many different perspectives. A very fruitful perspective is to think of such manifolds as dynamical objects. For example, a very important class of hyperbolic 3–manifolds are those arising as mapping tori of pseudo-Anosov automorphisms of surfaces (Thurston [24]). Such mapping tori naturally come with a flow, the suspension flow of the automorphism. In the seminal paper [4], Cannon and Thurston showed that this suspension flow can be chosen to be quasigeodesic and pseudo-Anosov. Informally, a flow on a hyperbolic 3–manifold is quasigeodesic if the lifted flowlines in the universal cover are quasi-geodesics in H3 , and a flow is pseudo-Anosov if it looks locally like a semi-branched cover of an Anosov flow. Of course, such a flow need not be transverse to a foliation by surfaces. See Thurston [24] and Fenley [6] for background and definitions. -
Invariant Manifolds and Their Interactions
Understanding the Geometry of Dynamics: Invariant Manifolds and their Interactions Hinke M. Osinga Department of Mathematics, University of Auckland, Auckland, New Zealand Abstract Dynamical systems theory studies the behaviour of time-dependent processes. For example, simulation of a weather model, starting from weather conditions known today, gives information about the weather tomorrow or a few days in advance. Dynamical sys- tems theory helps to explain the uncertainties in those predictions, or why it is pretty much impossible to forecast the weather more than a week ahead. In fact, dynamical sys- tems theory is a geometric subject: it seeks to identify critical boundaries and appropriate parameter ranges for which certain behaviour can be observed. The beauty of the theory lies in the fact that one can determine and prove many characteristics of such bound- aries called invariant manifolds. These are objects that typically do not have explicit mathematical expressions and must be found with advanced numerical techniques. This paper reviews recent developments and illustrates how numerical methods for dynamical systems can stimulate novel theoretical advances in the field. 1 Introduction Mathematical models in the form of systems of ordinary differential equations arise in numer- ous areas of science and engineering|including laser physics, chemical reaction dynamics, cell modelling and control engineering, to name but a few; see, for example, [12, 14, 35] as en- try points into the extensive literature. Such models are used to help understand how different types of behaviour arise under a variety of external or intrinsic influences, and also when system parameters are changed; examples include finding the bursting threshold for neurons, determin- ing the structural stability limit of a building in an earthquake, or characterising the onset of synchronisation for a pair of coupled lasers.