The Essence of Chaos
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Robust Learning of Chaotic Attractors
Robust Learning of Chaotic Attractors Rembrandt Bakker* Jaap C. Schouten Marc-Olivier Coppens Chemical Reactor Engineering Chemical Reactor Engineering Chemical Reactor Engineering Delft Univ. of Technology Eindhoven Univ. of Technology Delft Univ. of Technology [email protected]·nl [email protected] [email protected]·nl Floris Takens C. Lee Giles Cor M. van den Bleek Dept. Mathematics NEC Research Institute Chemical Reactor Engineering University of Groningen Princeton Nl Delft Univ. of Technology F. [email protected] [email protected] [email protected]·nl Abstract A fundamental problem with the modeling of chaotic time series data is that minimizing short-term prediction errors does not guarantee a match between the reconstructed attractors of model and experiments. We introduce a modeling paradigm that simultaneously learns to short-tenn predict and to locate the outlines of the attractor by a new way of nonlinear principal component analysis. Closed-loop predictions are constrained to stay within these outlines, to prevent divergence from the attractor. Learning is exceptionally fast: parameter estimation for the 1000 sample laser data from the 1991 Santa Fe time series competition took less than a minute on a 166 MHz Pentium PC. 1 Introduction We focus on the following objective: given a set of experimental data and the assumption that it was produced by a deterministic chaotic system, find a set of model equations that will produce a time-series with identical chaotic characteristics, having the same chaotic attractor. The common approach consists oftwo steps: (1) identify a model that makes accurate short tenn predictions; and (2) generate a long time-series with the model and compare the nonlinear-dynamic characteristics of this time-series with the original, measured time-series. -
Twenty Female Mathematicians Hollis Williams
Twenty Female Mathematicians Hollis Williams Acknowledgements The author would like to thank Alba Carballo González for support and encouragement. 1 Table of Contents Sofia Kovalevskaya ................................................................................................................................. 4 Emmy Noether ..................................................................................................................................... 16 Mary Cartwright ................................................................................................................................... 26 Julia Robinson ....................................................................................................................................... 36 Olga Ladyzhenskaya ............................................................................................................................. 46 Yvonne Choquet-Bruhat ....................................................................................................................... 56 Olga Oleinik .......................................................................................................................................... 67 Charlotte Fischer .................................................................................................................................. 77 Karen Uhlenbeck .................................................................................................................................. 87 Krystyna Kuperberg ............................................................................................................................. -
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 -
Alwyn C. Scott
the frontiers collection the frontiers collection Series Editors: A.C. Elitzur M.P. Silverman J. Tuszynski R. Vaas H.D. Zeh The books in this collection are devoted to challenging and open problems at the forefront of modern science, including related philosophical debates. In contrast to typical research monographs, however, they strive to present their topics in a manner accessible also to scientifically literate non-specialists wishing to gain insight into the deeper implications and fascinating questions involved. Taken as a whole, the series reflects the need for a fundamental and interdisciplinary approach to modern science. Furthermore, it is intended to encourage active scientists in all areas to ponder over important and perhaps controversial issues beyond their own speciality. Extending from quantum physics and relativity to entropy, consciousness and complex systems – the Frontiers Collection will inspire readers to push back the frontiers of their own knowledge. Other Recent Titles The Thermodynamic Machinery of Life By M. Kurzynski The Emerging Physics of Consciousness Edited by J. A. Tuszynski Weak Links Stabilizers of Complex Systems from Proteins to Social Networks By P. Csermely Quantum Mechanics at the Crossroads New Perspectives from History, Philosophy and Physics Edited by J. Evans, A.S. Thorndike Particle Metaphysics A Critical Account of Subatomic Reality By B. Falkenburg The Physical Basis of the Direction of Time By H.D. Zeh Asymmetry: The Foundation of Information By S.J. Muller Mindful Universe Quantum Mechanics and the Participating Observer By H. Stapp Decoherence and the Quantum-to-Classical Transition By M. Schlosshauer For a complete list of titles in The Frontiers Collection, see back of book Alwyn C. -
A Fairly Complete History and Tour of Aynho Village – Updated January 2017 Aynho Is a Two-Part Name
A Fairly Complete History and Tour of Aynho Village – updated January 2017 Aynho is a two-part name - ‘Ayn’ is either a corruption of a Saxon personal name, or more likely the Saxon word for a spring or stream. The ‘Hoh’ is a Saxon word for a promontory/projecting ridge of land standing on a plain as Aynho does. The earliest mention (in the Domesday Book) of an owner of the manor of Aynho is Asgar - a Danish thane (knight). He was standard bearer for Edward the Confessor who reigned from 1042 to 1066. (Edward was born at Islip about fifteen miles south east of Aynho, so he probably knew Asgar). The entry showed 3¼ hides (about 400 acres altogether), land for 8 ploughs, a mill and 20 acres of meadow. Why was Aynho so relatively important in the mid-ten hundreds? Probably because of its location high up overlooking the whole Cherwell valley. There were very few significant houses in existence within a radius of twenty miles at that time, and it is believed that Aynho had a substantial wooden Saxon manor house then. For example Oxford Castle was not built until 1073, Banbury Castle 1135, Broughton Castle 1300, Rousham House 1635 and Upton House 1695. The first proper Oxford College, University College, wasn’t founded until1249. Apart from Aynho north of Oxford only Sulgrave Manor is recorded as having an Anglo-Saxon Manor House around the late 9th century. William the Conqueror gave the village to one of his barons, Geoffrey de Mandeville, for helping him win the Battle of Hastings in 1066. -
Complex Numbers and Colors
Complex Numbers and Colors For the sixth year, “Complex Beauties” provides you with a look into the wonderful world of complex functions and the life and work of mathematicians who contributed to our understanding of this field. As always, we intend to reach a diverse audience: While most explanations require some mathemati- cal background on the part of the reader, we hope non-mathematicians will find our “phase portraits” exciting and will catch a glimpse of the richness and beauty of complex functions. We would particularly like to thank our guest authors: Jonathan Borwein and Armin Straub wrote on random walks and corresponding moment functions and Jorn¨ Steuding contributed two articles, one on polygamma functions and the second on almost periodic functions. The suggestion to present a Belyi function and the possibility for the numerical calculations came from Donald Marshall; the November title page would not have been possible without Hrothgar’s numerical solution of the Bla- sius equation. The construction of the phase portraits is based on the interpretation of complex numbers z as points in the Gaussian plane. The horizontal coordinate x of the point representing z is called the real part of z (Re z) and the vertical coordinate y of the point representing z is called the imaginary part of z (Im z); we write z = x + iy. Alternatively, the point representing z can also be given by its distance from the origin (jzj, the modulus of z) and an angle (arg z, the argument of z). The phase portrait of a complex function f (appearing in the picture on the left) arises when all points z of the domain of f are colored according to the argument (or “phase”) of the value w = f (z). -
Causal Inference for Process Understanding in Earth Sciences
Causal inference for process understanding in Earth sciences Adam Massmann,* Pierre Gentine, Jakob Runge May 4, 2021 Abstract There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth sys- tem to identify where causal inference may be most tractable and useful to address problems in Earth science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed por- tion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system’s state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples that causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties. Note: We will update this manuscript as our understanding of causality’s role in Earth sci- ence research evolves. Comments, feedback, and edits are enthusiastically encouraged, and we arXiv:2105.00912v1 [physics.ao-ph] 3 May 2021 will add acknowledgments and/or coauthors as we receive community contributions. -
Fundamental Theorems in Mathematics
SOME FUNDAMENTAL THEOREMS IN MATHEMATICS OLIVER KNILL Abstract. An expository hitchhikers guide to some theorems in mathematics. Criteria for the current list of 243 theorems are whether the result can be formulated elegantly, whether it is beautiful or useful and whether it could serve as a guide [6] without leading to panic. The order is not a ranking but ordered along a time-line when things were writ- ten down. Since [556] stated “a mathematical theorem only becomes beautiful if presented as a crown jewel within a context" we try sometimes to give some context. Of course, any such list of theorems is a matter of personal preferences, taste and limitations. The num- ber of theorems is arbitrary, the initial obvious goal was 42 but that number got eventually surpassed as it is hard to stop, once started. As a compensation, there are 42 “tweetable" theorems with included proofs. More comments on the choice of the theorems is included in an epilogue. For literature on general mathematics, see [193, 189, 29, 235, 254, 619, 412, 138], for history [217, 625, 376, 73, 46, 208, 379, 365, 690, 113, 618, 79, 259, 341], for popular, beautiful or elegant things [12, 529, 201, 182, 17, 672, 673, 44, 204, 190, 245, 446, 616, 303, 201, 2, 127, 146, 128, 502, 261, 172]. For comprehensive overviews in large parts of math- ematics, [74, 165, 166, 51, 593] or predictions on developments [47]. For reflections about mathematics in general [145, 455, 45, 306, 439, 99, 561]. Encyclopedic source examples are [188, 705, 670, 102, 192, 152, 221, 191, 111, 635]. -
Maps, Chaos, and Fractals
MATH305 Summer Research Project 2006-2007 Maps, Chaos, and Fractals Phillips Williams Department of Mathematics and Statistics University of Canterbury Maps, Chaos, and Fractals Phillipa Williams* MATH305 Mathematics Project University of Canterbury 9 February 2007 Abstract The behaviour and properties of one-dimensional discrete mappings are explored by writing Matlab code to iterate mappings and draw graphs. Fixed points, periodic orbits, and bifurcations are described and chaos is introduced using the logistic map. Symbolic dynamics are used to show that the doubling map and the logistic map have the properties of chaos. The significance of a period-3 orbit is examined and the concept of universality is introduced. Finally the Cantor Set provides a brief example of the use of iterative processes to generate fractals. *supervised by Dr. Alex James, University of Canterbury. 1 Introduction Devaney [1992] describes dynamical systems as "the branch of mathematics that attempts to describe processes in motion)) . Dynamical systems are mathematical models of systems that change with time and can be used to model either discrete or continuous processes. Contin uous dynamical systems e.g. mechanical systems, chemical kinetics, or electric circuits can be modeled by differential equations. Discrete dynamical systems are physical systems that involve discrete time intervals, e.g. certain types of population growth, daily fluctuations in the stock market, the spread of cases of infectious diseases, and loans (or deposits) where interest is compounded at fixed intervals. Discrete dynamical systems can be modeled by iterative maps. This project considers one-dimensional discrete dynamical systems. In the first section, the behaviour and properties of one-dimensional maps are examined using both analytical and graphical methods. -
Arxiv:1812.05143V1 [Math.AT] 28 Nov 2018 While Studying a Simplified Model (1) for Weather Forecasting [18]
TOPOLOGICAL TIME SERIES ANALYSIS JOSE A. PEREA Abstract. Time series are ubiquitous in our data rich world. In what fol- lows I will describe how ideas from dynamical systems and topological data analysis can be combined to gain insights from time-varying data. We will see several applications to the live sciences and engineering, as well as some of the theoretical underpinnings. 1. Lorenz and the butterfly Imagine you have a project involving a crucial computer simulation. For an 3 3 initial value v0 = (x0; y0; z0) 2 R , a sequence v0;:::; vn 2 R is computed in such a way that vj+1 is determined from vj for j = 0; : : : ; n − 1. After the simulation is complete you realize that a rerun is needed for further analysis. Instead of initializing at v0, which might take a while, you take a shortcut: you input a value vj selected from the middle of the current results, and the simulation runs from there while you go for coffee. Figure1 is displayed on the computer monitor upon your return; the orange curve is the sequence x0; : : : ; xn from the initial simulation, and the blue curve is the x coordinate for the rerun initialized at vj: 25 30 35 40 45 50 55 60 65 70 75 Figure 1. Orange: results from the simulation initialized at v0; blue: results after manually restarting the simulation from vj. The results agree at first, but then they diverge widely; what is going on? Edward Norton Lorenz, a mathematical meteorologist, asked himself the very same question arXiv:1812.05143v1 [math.AT] 28 Nov 2018 while studying a simplified model (1) for weather forecasting [18]. -
Math Morphing Proximate and Evolutionary Mechanisms
Curriculum Units by Fellows of the Yale-New Haven Teachers Institute 2009 Volume V: Evolutionary Medicine Math Morphing Proximate and Evolutionary Mechanisms Curriculum Unit 09.05.09 by Kenneth William Spinka Introduction Background Essential Questions Lesson Plans Website Student Resources Glossary Of Terms Bibliography Appendix Introduction An important theoretical development was Nikolaas Tinbergen's distinction made originally in ethology between evolutionary and proximate mechanisms; Randolph M. Nesse and George C. Williams summarize its relevance to medicine: All biological traits need two kinds of explanation: proximate and evolutionary. The proximate explanation for a disease describes what is wrong in the bodily mechanism of individuals affected Curriculum Unit 09.05.09 1 of 27 by it. An evolutionary explanation is completely different. Instead of explaining why people are different, it explains why we are all the same in ways that leave us vulnerable to disease. Why do we all have wisdom teeth, an appendix, and cells that if triggered can rampantly multiply out of control? [1] A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole," a property called self-similarity. The term was coined by Beno?t Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion. http://www.kwsi.com/ynhti2009/image01.html A fractal often has the following features: 1. It has a fine structure at arbitrarily small scales. -
Chaos Theory: the Essential for Military Applications
U.S. Naval War College U.S. Naval War College Digital Commons Newport Papers Special Collections 10-1996 Chaos Theory: The Essential for Military Applications James E. Glenn Follow this and additional works at: https://digital-commons.usnwc.edu/usnwc-newport-papers Recommended Citation Glenn, James E., "Chaos Theory: The Essential for Military Applications" (1996). Newport Papers. 10. https://digital-commons.usnwc.edu/usnwc-newport-papers/10 This Book is brought to you for free and open access by the Special Collections at U.S. Naval War College Digital Commons. It has been accepted for inclusion in Newport Papers by an authorized administrator of U.S. Naval War College Digital Commons. For more information, please contact [email protected]. The Newport Papers Tenth in the Series CHAOS ,J '.' 'l.I!I\'lt!' J.. ,\t, ,,1>.., Glenn E. James Major, U.S. Air Force NAVAL WAR COLLEGE Chaos Theory Naval War College Newport, Rhode Island Center for Naval Warfare Studies Newport Paper Number Ten October 1996 The Newport Papers are extended research projects that the editor, the Dean of Naval Warfare Studies, and the President of the Naval War CoJIege consider of particular in terest to policy makers, scholars, and analysts. Papers are drawn generally from manuscripts not scheduled for publication either as articles in the Naval War CollegeReview or as books from the Naval War College Press but that nonetheless merit extensive distribution. Candidates are considered by an edito rial board under the auspices of the Dean of Naval Warfare Studies. The views expressed in The Newport Papers are those of the authors and not necessarily those of the Naval War College or the Department of the Navy.