On Floris Takens and Our Joint Mathematical Work
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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. -
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 -
TWAS an Rep IMP
TWAS gratefully acknowledges the financial support for its 2008 TWAS, the academy of sciences for the developing world, is an activities provided mainly by the following: autonomous international organization that promotes scientific capacity • the Ministry of Foreign Affairs, Italy and excellence in the South. Founded in 1983 by a group of eminent • the Swedish International Development Agency (Sida) scientists under the leadership of the late Nobel laureate Abdus Salam • illycaffè, S.p.A., Trieste, Italy of Pakistan, TWAS was officially launched in Trieste, Italy, in 1985, • the Mexican Academy of Sciences by the secretary-general of the United Nations. • Microsoft Research Ltd., UK • the European Union, Seventh Framework Programme (FP7) TWAS has 909 members from some 90 countries, more than 70 of which • the Kuwait Foundation for the Advancement of Sciences (KFAS) are developing countries. A Council of 13 members is responsible for • the Wellcome Trust, UK supervising all Academy affairs. It is assisted in the administration and coordination of programmes by a secretariat, headed by the executive director. The secretariat is located on the premises of the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy. The administration and financial operation of TWAS is undertaken by T W the United Nations Educational, Scientific and Cultural Organization (UNESCO) in accordance with an agreement signed by the two A S organizations. A major portion of TWAS funding is provided by the annual report Ministry of Foreign -
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]. -
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]. -
Twas Anniversary 3 Y E a R 2 0 0 8 Vol.20 No.3
TH TWAS ANNIVERSARY 3 Y E A R 2 0 0 8 VOL.20 NO.3 TWAS nN E W S L E T TeE R O F TwH E AC A D E MsY O F SlC I E NeC E S F O Rt T H E DtE V E L OeP I N G WOrR L D Published with the support of the Kuwait Foundation for the Advancement of Sciences EDITORIAL TWAS NEWSLETTER Published quarterly with the support of the Kuwait Foundation TWAS WILL BE 25 YEARS OLD THIS YEAR. A GALA CELEBRATION, SUPPORTED for the Advancement of Sciences (KFAS) by TWAS, the academy of BY THE MEXICAN GOVERNMENT AND HOSTED BY THE MEXICAN ACADEMY OF sciences for the developing world SCIENCES, WILL TAKE PLACE IN MEXICO CITY FROM 10 TO 13 NOVEMBER. ICTP Campus, Strada Costiera 11 34014 Trieste, Italy MORE THAN 300 SCIENTISTS WILL BE IN ATTENDANCE. tel: +39 040 2240327 fax: +39 040 224559 e-mail: [email protected] he four-day event will provide another opportunity for the Academy to examine its website: www.twas.org T past and to explore its future – all within the context of TWAS’s quarter century of experience in promoting science and science-based development in the South. TWAS COUNCIL In the following article, TWAS president Jacob Palis reviews the challenges that the President Academy has faced since its inception. Equally important, he analyses the issues that lie Jacob Palis (Brazil) ahead for the Academy – an institution that now occupies a central place in the world of Immediate Past President C.N.R. -
A Probabilistic Takens Theorem
A PROBABILISTIC TAKENS THEOREM KRZYSZTOF BARAŃSKI1, YONATAN GUTMAN2, AND ADAM ŚPIEWAK1 Abstract. Let X RN be a Borel set, µ a Borel probability measure on X and T : X X ⊂ → a locally Lipschitz and injective map. Fix k N strictly greater than the (Hausdorff) dimen- ∈ sion of X and assume that the set of p-periodic points of T has dimension smaller than p for p = 1,...,k 1. We prove that for a typical polynomial perturbation h˜ of a given locally − Lipschitz function h: X R, the k-delay coordinate map x (h˜(x), h˜(T x),..., h˜(T k−1x)) → 7→ is injective on a set of full µ-measure. This is a probabilistic version of the Takens de- lay embedding theorem as proven by Sauer, Yorke and Casdagli. We also provide a non- dynamical probabilistic embedding theorem of similar type, which strengthens a previous result by Alberti, Bölcskei, De Lellis, Koliander and Riegler. In both cases, the key improve- ments compared to the non-probabilistic counterparts are the reduction of the number of required coordinates from 2 dim X to dim X and using Hausdorff dimension instead of the box-counting one. We present examples showing how the use of the Hausdorff dimension improves the previously obtained results. 1. Introduction Consider an experimentalist observing a physical system modeled by a discrete time dy- namical system (X, T ), where T : X X is the evolution rule and the phase space X is a → subset of the Euclidean space RN . It often happens that, for a given point x X, instead ∈ of an actual sequence of k states x,Tx,...,T k−1x, the observer’s access is limited to the values of k measurements h(x), h(T x),...,h(T k−1x), for a real-valued observable h: X R. -
Turbulence DAVID RUELLE and FLORIS TAKENS* I.H.E.S., Bures-Sur-Yvette, France
Commun. math. Phys. 20, 167—192 (1971) © by Springer-Verlag 1971 On the Nature of Turbulence DAVID RUELLE and FLORIS TAKENS* I.H.E.S., Bures-sur-Yvette, France Received October 5, 1970 Abstract. A mechanism for the generation of turbulence and related phenomena in dissipative systems is proposed. § 1. Introduction If a physical system consisting of a viscous fluid (and rigid bodies) is not subjected to any external action, it will tend to a state of rest (equilibrium). We submit now the system to a steady action (pumping, heating, etc.) measured by a parameter μ1 . When μ = 0 the fluid is at rest. For μ>0 we obtain first a steady state, i.e., the physical parameters describing the fluid at any point (velocity, temperature, etc.) are constant in time, but the fluid is no longer in equilibrium. This steady situation prevails for small values of μ. When μ is increased various new phenomena occur; (a) the fluid motion may remain steady but change its symmetry pattern; (b) the fluid motion may become periodic in time; (c) for suffi- ciently large μ, the fluid motion becomes very complicated, irregular and chaotic, we have turbulence. The physical phenomenon of turbulent fluid motion has received various mathematical interpretations. It has been argued by Leray [9] that it leads to a breakdown of the validity of the equations (Navier- Stokes) used to describe the system. While such a breakdown may happen we think that it does not necessarily accompany turbulence. Landau and Lifschitz [8] propose that the physical parameters x describing a fluid in turbulent motion are quasi-periodic functions of time: where /has period 1 in each of its arguments separately and the frequences 2 ωl5 ..., ωk are not rationally related . -
Notices of the American Mathematical Society
Society c :s ~ CALENDAR OF AMS MEETINGS THIS CALENDAR lists all meetings which have been approved by the Council prior to the date this issue of the Notices was sent to press. The summer and annual meetings are joint meetings of the Mathematical Association of America and the American Mathematical Society. The meeting dates which fall rather far in the future are subject to change; this is particularly true of meetings to which no numbers have yet been assigned. Programs of the meet ings will appear in the issues indicated below. First and second announcements of the meetings will have appeared in earlier issues. ABSTRACTS OF PAPERS presented at a meeting of the Society are published in the journal Abstracts of papers presented to the American Mathematical Society in the issue corresponding to that of the Notices which contains the program of the meeting. Abstracts should be submitted on special forms which are available in many depart ments of mathematics and from the office of the Society in Providence. Abstracts of papers to be presented at the meeting must be received at the headquarters of the Society in Providence, Rhode Island, on or before the deadline given below for the meeting. Note that the deadline for abstracts submitted for consideration for presentation at special sessions is usually three weeks earlier than that specified below. For additional information consult the meet· ing announcement and the Jist of organizers of special sessions. MEETING ABSTRACT NUMBER DATE PLACE DEADLINE ISSUE 779 August 18-22, 1980 Ann Arbor, -
From Chaos to Early Warnings NAW 5/2 Nr
Floris Takens From chaos to early warnings NAW 5/2 nr. 4 december 2001 317 Floris Takens Mathematisch Instituut, Universiteit Groningen Postbus 800, 9700 AV Groningen F. [email protected] Afscheidsrede Floris Takens From chaos to early warnings Een van de grondleggers van het moderne vakgebied ’dynamische the distance between the first ball (which we hit directly) and the systemen’, Floris Takens, werd onlangs zestig jaar. Tegelijkertijd ging second ball, with which the first collision is to be made, is denot- hij met emeritaat. Ter ere van zijn bijdragen aan de internationale ed by ℓ and if the diameter of the balls is denoted by d, then, in ontwikkeling van de dynamische systemen, werd in Groningen van order to realize this first collision, we need to give the first ball an 25–29 juni een workshop ‘Global analysis of dynamical systems’ initial velocity whose direction is in a sector with opening angle georganiseerd. approximately 2d/ℓ (radians). This is illustrated in figure 1. So a Op 30 juni werd deze viering besloten met een publiek evenement deviation in the initial direction up to d/ℓ can cause the ball to go, in de Senaatszaal van de Universiteit Groningen. Jacob Palis, presi- after the first collision, anywhere; this means that ℓ/d is a mea- dent van de International Mathematical Union, vriend en collega met sure of the amplification of small deviations in the velocity which wie Takens veel heeft samengewerkt, sprak in de eerste lezing zijn occurs at the first collision. We shall call this amplification factor lof uit over het werk en de persoon van Floris Takens. -
Finite Approximation of Sinai-Bowen-Ruelle Measures For
Finite Approximation of SinaiBowenRuelle Measures for Anosov Systems in Two Dimensions Gary Froyland Department of Mathematics The University of Western Australia Nedlands WA AUSTRALIA Abstract We describ e a computational metho d of approximating the physical or SinaiBowenRuelle measure of an Anosov system in two dimensions The approximation may either b e viewed as a xed p oint of an approximate PerronFrob enius op erator or as an invariant measure of a randomly p erturb ed system Keywords invariant measure PerronFrob enius op erator small random p erturbation Mathematics Subject Classication Primary F Secondary F D A Intro duction The existence and computation of imp ortant invariant measures of deterministic dynamical systems are still ma jor concerns in ergo dic theory In this note we do not address the problem of existence but provide a small step in the computation of imp ortant measures when they are known to exist In one dimension absolutely continuous invariant measures are considered to b e imp ortant from a computational p oint of view b ecause it is absolutely continuous measures that show up on computer simulations for most starting p oints If is an ergo dic absolutely continuous invariant measure for then by the Birkho Theorem we have any Borel measurable f Z n X i lim g d g f x n n i for almost all x and any given continuous g R Since Leb we may as well say that holds for Leb esgue almost all x is clearly the unique measure with this prop erty In this sense it seems natural to call absolutely