Exploding the Dark Heart of Chaos: Part I
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Chapter 6: Ensemble Forecasting and Atmospheric Predictability
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors and errors in the initial conditions… In the 1960’s the forecasts were skillful for only one day or so. Statistical prediction was equal or better than dynamical predictions, Like it was until now for ENSO predictions! Lorenz wanted to show that statistical prediction could not match prediction with a nonlinear model for the Tokyo (1960) NWP conference So, he tried to find a model that was not periodic (otherwise stats would win!) He programmed in machine language on a 4K memory, 60 ops/sec Royal McBee computer He developed a low-order model (12 d.o.f) and changed the parameters and eventually found a nonperiodic solution Printed results with 3 significant digits (plenty!) Tried to reproduce results, went for a coffee and OOPS! Lorenz (1963) discovered that even with a perfect model and almost perfect initial conditions the forecast loses all skill in a finite time interval: “A butterfly in Brazil can change the forecast in Texas after one or two weeks”. In the 1960’s this was only of academic interest: forecasts were useless in two days Now, we are getting closer to the 2 week limit of predictability, and we have to extract the maximum information Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability (chaos) b) Stable systems are infinitely predictable a) Unstable dynamical system b) Stable dynamical -
Invariant Measures for Hyperbolic Dynamical Systems
Invariant measures for hyperbolic dynamical systems N. Chernov September 14, 2006 Contents 1 Markov partitions 3 2 Gibbs Measures 17 2.1 Gibbs states . 17 2.2 Gibbs measures . 33 2.3 Properties of Gibbs measures . 47 3 Sinai-Ruelle-Bowen measures 56 4 Gibbs measures for Anosov and Axiom A flows 67 5 Volume compression 86 6 SRB measures for general diffeomorphisms 92 This survey is devoted to smooth dynamical systems, which are in our context diffeomorphisms and smooth flows on compact manifolds. We call a flow or a diffeomorphism hyperbolic if all of its Lyapunov exponents are different from zero (except the one in the flow direction, which has to be 1 zero). This means that the tangent vectors asymptotically expand or con- tract exponentially fast in time. For many reasons, it is convenient to assume more than just asymptotic expansion or contraction, namely that the expan- sion and contraction of tangent vectors happens uniformly in time. Such hyperbolic systems are said to be uniformly hyperbolic. Historically, uniformly hyperbolic flows and diffeomorphisms were stud- ied as early as in mid-sixties: it was done by D. Anosov [2] and S. Smale [77], who introduced his Axiom A. In the seventies, Anosov and Axiom A dif- feomorphisms and flows attracted much attention from different directions: physics, topology, and geometry. This actually started in 1968 when Ya. Sinai constructed Markov partitions [74, 75] that allowed a symbolic representa- tion of the dynamics, which matched the existing lattice models in statistical mechanics. As a result, the theory of Gibbs measures for one-dimensional lat- tices was carried over to Anosov and Axiom A dynamical systems. -
Using Fractal Dimension for Target Detection in Clutter
KIM T. CONSTANTIKES USING FRACTAL DIMENSION FOR TARGET DETECTION IN CLUTTER The detection of targets in natural backgrounds requires that we be able to compute some characteristic of target that is distinct from background clutter. We assume that natural objects are fractals and that the irregularity or roughness of the natural objects can be characterized with fractal dimension estimates. Since man-made objects such as aircraft or ships are comparatively regular and smooth in shape, fractal dimension estimates may be used to distinguish natural from man-made objects. INTRODUCTION Image processing associated with weapons systems is fractal. Falconer1 defines fractals as objects with some or often concerned with methods to distinguish natural ob all of the following properties: fine structure (i.e., detail jects from man-made objects. Infrared seekers in clut on arbitrarily small scales) too irregular to be described tered environments need to distinguish the clutter of with Euclidean geometry; self-similar structure, with clouds or solar sea glint from the signature of the intend fractal dimension greater than its topological dimension; ed target of the weapon. The discrimination of target and recursively defined. This definition extends fractal from clutter falls into a category of methods generally into a more physical and intuitive domain than the orig called segmentation, which derives localized parameters inal Mandelbrot definition whereby a fractal was a set (e.g.,texture) from the observed image intensity in order whose "Hausdorff-Besicovitch dimension strictly exceeds to discriminate objects from background. Essentially, one its topological dimension.,,2 The fine, irregular, and self wants these parameters to be insensitive, or invariant, to similar structure of fractals can be experienced firsthand the kinds of variation that the objects and background by looking at the Mandelbrot set at several locations and might naturally undergo because of changes in how they magnifications. -
Complexity” Makes a Difference: Lessons from Critical Systems Thinking and the Covid-19 Pandemic in the UK
systems Article How We Understand “Complexity” Makes a Difference: Lessons from Critical Systems Thinking and the Covid-19 Pandemic in the UK Michael C. Jackson Centre for Systems Studies, University of Hull, Hull HU6 7TS, UK; [email protected]; Tel.: +44-7527-196400 Received: 11 November 2020; Accepted: 4 December 2020; Published: 7 December 2020 Abstract: Many authors have sought to summarize what they regard as the key features of “complexity”. Some concentrate on the complexity they see as existing in the world—on “ontological complexity”. Others highlight “cognitive complexity”—the complexity they see arising from the different interpretations of the world held by observers. Others recognize the added difficulties flowing from the interactions between “ontological” and “cognitive” complexity. Using the example of the Covid-19 pandemic in the UK, and the responses to it, the purpose of this paper is to show that the way we understand complexity makes a huge difference to how we respond to crises of this type. Inadequate conceptualizations of complexity lead to poor responses that can make matters worse. Different understandings of complexity are discussed and related to strategies proposed for combatting the pandemic. It is argued that a “critical systems thinking” approach to complexity provides the most appropriate understanding of the phenomenon and, at the same time, suggests which systems methodologies are best employed by decision makers in preparing for, and responding to, such crises. Keywords: complexity; Covid-19; critical systems thinking; systems methodologies 1. Introduction No one doubts that we are, in today’s world, entangled in complexity. At the global level, economic, social, technological, health and ecological factors have become interconnected in unprecedented ways, and the consequences are immense. -
Mathematics Calendar
Mathematics Calendar The most comprehensive and up-to-date Mathematics Calendar information is available on e-MATH at http://www.ams.org/mathcal/. August 2006 wide to discuss the most recent developments in anomalous energy (heat) transport in low dimensional systems, synchronization of 1–5 Ninth Meeting of New Researchers in Statistics and Proba- chaotic systems and applications to communication of information. bility,University of Washington, Seattle, Washington. (Mar. 2006, It also serves as a forum to promote regional as well as international p. 379) scientific exchange and collaboration. Description:TheIMSCommittee on New Researchers is organizing ameeting of recent Ph.D. recipients in Statistics and Probability. Information and registration: http://www.ims.nus.edu.sg/ The purpose of the conference is to promote interaction among new Programs/chaos/;email:[email protected] on researchers primarily by introducing them to each other’s research scientific aspects of the program, please email Baowen Li at in an informal setting. As part of the conference, participants will [email protected]. present talks and posters on their research and discuss interests and 2–4 31st Sapporo Symposium on Partial Differential Equations, professional experiences over meals and social activities organized Department of Mathematics, Hokkaido University, Sapporo, Japan. through the meeting as well as by the participants themselves. The (Jan. 2006, p. 70) relationships established in this informal collegiate setting among Description:TheSapporo Symposium on Partial Differential Equa- junior researchers are ones that maylastacareer (lifetime?!) The tions has been held annually to present the latest developments on meeting is to be held prior to the 2006 Joint Statistical Meetings in PDE with a broad spectrum of interests not limited to the methods Seattle, WA. -
Equilibrium States and the Ergodic Theory of Anosov Diffeomorphisms
Rufus Bowen Equilibrium States and the Ergodic Theory of Anosov Diffeomorphisms New edition of Lect. Notes in Math. 470, Springer, 1975. April 14, 2013 Springer Preface The Greek and Roman gods, supposedly, resented those mortals endowed with superlative gifts and happiness, and punished them. The life and achievements of Rufus Bowen (1947{1978) remind us of this belief of the ancients. When Rufus died unexpectedly, at age thirty-one, from brain hemorrhage, he was a very happy and successful man. He had great charm, that he did not misuse, and superlative mathematical talent. His mathematical legacy is important, and will not be forgotten, but one wonders what he would have achieved if he had lived longer. Bowen chose to be simple rather than brilliant. This was the hard choice, especially in a messy subject like smooth dynamics in which he worked. Simplicity had also been the style of Steve Smale, from whom Bowen learned dynamical systems theory. Rufus Bowen has left us a masterpiece of mathematical exposition: the slim volume Equilibrium States and the Ergodic Theory of Anosov Diffeomorphisms (Springer Lecture Notes in Mathematics 470 (1975)). Here a number of results which were new at the time are presented in such a clear and lucid style that Bowen's monograph immediately became a classic. More than thirty years later, many new results have been proved in this area, but the volume is as useful as ever because it remains the best introduction to the basics of the ergodic theory of hyperbolic systems. The area discussed by Bowen came into existence through the merging of two apparently unrelated theories. -
Claus Kahlert and Otto E. Rössler Institute for Physical and Theoretical Chemistry, University of Tübingen Z. Naturforsch
Chaos as a Limit in a Boundary Value Problem Claus Kahlert and Otto E. Rössler Institute for Physical and Theoretical Chemistry, University of Tübingen Z. Naturforsch. 39a, 1200- 1203 (1984); received November 8, 1984 A piecewise-linear. 3-variable autonomous O.D.E. of C° type, known to describe constant- shape travelling waves in one-dimensional reaction-diffusion media of Rinzel-Keller type, is numerically shown to possess a chaotic attractor in state space. An analytical method proving the possibility of chaos is outlined and a set of parameters yielding Shil'nikov chaos indicated. A symbolic dynamics technique can be used to show how the limiting chaos dominates the behavior even of the finite boundary value problem. Reaction-diffusion equations occur in many dis boundary conditions are assumed. This result was ciplines [1, 2], Piecewise-linear systems are especially obtained by an analytical matching method. The amenable to analysis. A variant to the Rinzel-Keller connection to chaos theory (Smale [6] basic sets) equation of nerve conduction [3] can be written as was not evident at the time. In the following, even the possibility of manifest chaos will be demon 9 ö2 — u u + p [- u + r - ß + e (ii - Ö)], strated. o t oa~ In Fig. 1, a chaotic attractor of (2) is presented. Ö The flow can be classified as an example of "screw- — r = - e u + r , (1) 0/ type-chaos" (cf. [7]). A second example of a chaotic attractor is shown in Fig. 2. A 1-D projection of a where 0(a) =1 if a > 0 and zero otherwise; d is the 2-D cross section through the chaotic flow in a threshold parameter. -
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music. Talk given at PoRT workshop, Glasgow, July 2012 Fred Cummins, University College Dublin [1] Dynamical Systems Theory (DST) is the lingua franca of Physics (both Newtonian and modern), Biology, Chemistry, and many other sciences and non-sciences, such as Economics. To employ the tools of DST is to take an explanatory stance with respect to observed phenomena. DST is thus not just another tool in the box. Its use is a different way of doing science. DST is increasingly used in non-computational, non-representational, non-cognitivist approaches to understanding behavior (and perhaps brains). (Embodied, embedded, ecological, enactive theories within cognitive science.) [2] DST originates in the science of mechanics, developed by the (co-)inventor of the calculus: Isaac Newton. This revolutionary science gave us the seductive concept of the mechanism. Mechanics seeks to provide a deterministic account of the relation between the motions of massive bodies and the forces that act upon them. A dynamical system comprises • A state description that indexes the components at time t, and • A dynamic, which is a rule governing state change over time The choice of variables defines the state space. The dynamic associates an instantaneous rate of change with each point in the state space. Any specific instance of a dynamical system will trace out a single trajectory in state space. (This is often, misleadingly, called a solution to the underlying equations.) Description of a specific system therefore also requires specification of the initial conditions. In the domain of mechanics, where we seek to account for the motion of massive bodies, we know which variables to choose (position and velocity). -
WHAT IS a CHAOTIC ATTRACTOR? 1. Introduction J. Yorke Coined the Word 'Chaos' As Applied to Deterministic Systems. R. Devane
WHAT IS A CHAOTIC ATTRACTOR? CLARK ROBINSON Abstract. Devaney gave a mathematical definition of the term chaos, which had earlier been introduced by Yorke. We discuss issues involved in choosing the properties that characterize chaos. We also discuss how this term can be combined with the definition of an attractor. 1. Introduction J. Yorke coined the word `chaos' as applied to deterministic systems. R. Devaney gave the first mathematical definition for a map to be chaotic on the whole space where a map is defined. Since that time, there have been several different definitions of chaos which emphasize different aspects of the map. Some of these are more computable and others are more mathematical. See [9] a comparison of many of these definitions. There is probably no one best or correct definition of chaos. In this paper, we discuss what we feel is one of better mathematical definition. (It may not be as computable as some of the other definitions, e.g., the one by Alligood, Sauer, and Yorke.) Our definition is very similar to the one given by Martelli in [8] and [9]. We also combine the concepts of chaos and attractors and discuss chaotic attractors. 2. Basic definitions We start by giving the basic definitions needed to define a chaotic attractor. We give the definitions for a diffeomorphism (or map), but those for a system of differential equations are similar. The orbit of a point x∗ by F is the set O(x∗; F) = f Fi(x∗) : i 2 Z g. An invariant set for a diffeomorphism F is an set A in the domain such that F(A) = A. -
Complex Bifurcation Structures in the Hindmarsh–Rose Neuron Model
International Journal of Bifurcation and Chaos, Vol. 17, No. 9 (2007) 3071–3083 c World Scientific Publishing Company COMPLEX BIFURCATION STRUCTURES IN THE HINDMARSH–ROSE NEURON MODEL J. M. GONZALEZ-MIRANDA´ Departamento de F´ısica Fundamental, Universidad de Barcelona, Avenida Diagonal 647, Barcelona 08028, Spain Received June 12, 2006; Revised October 25, 2006 The results of a study of the bifurcation diagram of the Hindmarsh–Rose neuron model in a two-dimensional parameter space are reported. This diagram shows the existence and extent of complex bifurcation structures that might be useful to understand the mechanisms used by the neurons to encode information and give rapid responses to stimulus. Moreover, the information contained in this phase diagram provides a background to develop our understanding of the dynamics of interacting neurons. Keywords: Neuronal dynamics; neuronal coding; deterministic chaos; bifurcation diagram. 1. Introduction 1961] has proven to give a good qualitative descrip- There are two main problems in neuroscience whose tion of the dynamics of the membrane potential of solution is linked to the research in nonlinear a single neuron, which is the most relevant experi- dynamics and chaos. One is the problem of inte- mental output of the neuron dynamics. Because of grated behavior of the nervous system [Varela et al., the enormous development that the field of dynam- 2001], which deals with the understanding of the ical systems and chaos has undergone in the last mechanisms that allow the different parts and units thirty years [Hirsch et al., 2004; Ott, 2002], the of the nervous system to work together, and appears study of meaningful mathematical systems like the related to the synchronization of nonlinear dynami- Hindmarsh–Rose model have acquired new interest cal oscillators. -
The Age of Addiction David T. Courtwright Belknap (2019) Opioids
The Age of Addiction David T. Courtwright Belknap (2019) Opioids, processed foods, social-media apps: we navigate an addictive environment rife with products that target neural pathways involved in emotion and appetite. In this incisive medical history, David Courtwright traces the evolution of “limbic capitalism” from prehistory. Meshing psychology, culture, socio-economics and urbanization, it’s a story deeply entangled in slavery, corruption and profiteering. Although reform has proved complex, Courtwright posits a solution: an alliance of progressives and traditionalists aimed at combating excess through policy, taxation and public education. Cosmological Koans Anthony Aguirre W. W. Norton (2019) Cosmologist Anthony Aguirre explores the nature of the physical Universe through an intriguing medium — the koan, that paradoxical riddle of Zen Buddhist teaching. Aguirre uses the approach playfully, to explore the “strange hinterland” between the realities of cosmic structure and our individual perception of them. But whereas his discussions of time, space, motion, forces and the quantum are eloquent, the addition of a second framing device — a fictional journey from Enlightenment Italy to China — often obscures rather than clarifies these chewy cosmological concepts and theories. Vanishing Fish Daniel Pauly Greystone (2019) In 1995, marine biologist Daniel Pauly coined the term ‘shifting baselines’ to describe perceptions of environmental degradation: what is viewed as pristine today would strike our ancestors as damaged. In these trenchant essays, Pauly trains that lens on fisheries, revealing a global ‘aquacalypse’. A “toxic triad” of under-reported catches, overfishing and deflected blame drives the crisis, he argues, complicated by issues such as the fishmeal industry, which absorbs a quarter of the global catch. -
D'arcy Wentworth Thompson
D’ARCY WENTWORTH THOMPSON Mathematically trained maverick zoologist D’Arcy Wentworth Thompson (May 2, 1860 –June 21, 1948) was among the first to cross the frontier between mathematics and the biological world and as such became the first true biomathematician. A polymath with unbounded energy, he saw mathematical patterns in everything – the mysterious spiral forms that appear in the curve of a seashell, the swirl of water boiling in a pan, the sweep of faraway nebulae, the thickness of stripes along a zebra’s flanks, the floret of a flower, etc. His premise was that “everything is the way it is because it got that way… the form of an object is a ‘diagram of forces’, in this sense, at least, that from it we can judge of or deduce the forces that are acting or have acted upon it.” He asserted that one must not merely study finished forms but also the forces that mold them. He sought to describe the mathematical origins of shapes and structures in the natural world, writing: “Cell and tissue, shell and bone, leaf and flower, are so many portions of matter and it is in obedience to the laws of physics that their particles have been moved, molded and conformed. There are no exceptions to the rule that God always geometrizes.” Thompson was born in Edinburgh, Scotland, the son of a Professor of Greek. At ten he entered Edinburgh Academy, winning prizes for Classics, Greek Testament, Mathematics and Modern Languages. At seventeen he went to the University of Edinburgh to study medicine, but two years later he won a scholarship to Trinity College, Cambridge, where he concentrated on zoology and natural science.