Predictability of Chaotic Dynamics

Predictability of Chaotic Dynamics
Author: Juan C. Vallejo,Miguel A. F. Sanjuan
Publsiher: Springer Nature
Total Pages: 196
Release: 2019-10-25
Genre: Science
ISBN: 9783030286309

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This book is primarily concerned with the computational aspects of predictability of dynamical systems - in particular those where observations, modeling and computation are strongly interdependent. Unlike with physical systems under control in laboratories, in astronomy it is uncommon to have the possibility of altering the key parameters of the studied objects. Therefore, the numerical simulations offer an essential tool for analysing these systems, and their reliability is of ever-increasing interest and importance. In this interdisciplinary scenario, the underlying physics provide the simulated models, nonlinear dynamics provides their chaoticity and instability properties, and the computer sciences provide the actual numerical implementation. This book introduces and explores precisely this link between the models and their predictability characterization based on concepts derived from the field of nonlinear dynamics, with a focus on the strong sensitivity to initial conditions and the use of Lyapunov exponents to characterize this sensitivity. This method is illustrated using several well-known continuous dynamical systems, such as the Contopoulos, Hénon-Heiles and Rössler systems. This second edition revises and significantly enlarges the material of the first edition by providing new entry points for discussing new predictability issues on a variety of areas such as machine decision-making, partial differential equations or the analysis of attractors and basins. Finally, the parts of the book devoted to the application of these ideas to astronomy have been greatly enlarged, by first presenting some basics aspects of predictability in astronomy and then by expanding these ideas to a detailed analysis of a galactic potential.

Chaos Detection and Predictability

Chaos Detection and Predictability
Author: Charalampos (Haris) Skokos,Georg A. Gottwald,Jacques Laskar
Publsiher: Springer
Total Pages: 269
Release: 2016-03-04
Genre: Science
ISBN: 9783662484104

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Distinguishing chaoticity from regularity in deterministic dynamical systems and specifying the subspace of the phase space in which instabilities are expected to occur is of utmost importance in as disparate areas as astronomy, particle physics and climate dynamics. To address these issues there exists a plethora of methods for chaos detection and predictability. The most commonly employed technique for investigating chaotic dynamics, i.e. the computation of Lyapunov exponents, however, may suffer a number of problems and drawbacks, for example when applied to noisy experimental data. In the last two decades, several novel methods have been developed for the fast and reliable determination of the regular or chaotic nature of orbits, aimed at overcoming the shortcomings of more traditional techniques. This set of lecture notes and tutorial reviews serves as an introduction to and overview of modern chaos detection and predictability techniques for graduate students and non-specialists. The book covers theoretical and computational aspects of traditional methods to calculate Lyapunov exponents, as well as of modern techniques like the Fast (FLI), the Orthogonal (OFLI) and the Relative (RLI) Lyapunov Indicators, the Mean Exponential Growth factor of Nearby Orbits (MEGNO), the Smaller (SALI) and the Generalized (GALI) Alignment Index and the ‘0-1’ test for chaos.

Deep Learning in Multi step Prediction of Chaotic Dynamics

Deep Learning in Multi step Prediction of Chaotic Dynamics
Author: Matteo Sangiorgio,Fabio Dercole,Giorgio Guariso
Publsiher: Springer Nature
Total Pages: 111
Release: 2022-02-14
Genre: Mathematics
ISBN: 9783030944827

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The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.

Predictability Stability and Chaos in N Body Dynamical Systems

Predictability  Stability  and Chaos in N Body Dynamical Systems
Author: Archie E. Roy
Publsiher: Springer Science & Business Media
Total Pages: 581
Release: 2012-12-06
Genre: Science
ISBN: 9781468459975

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The reader will find in this volume the Proceedings of the NATO Advanced Study Institute held in Cortina d'Ampezzo, Italy between August 6 and August 17, 1990 under the title "Predictability, Stability, and Chaos in N-Body Dynamical Systems". The Institute was the latest in a series held at three-yearly inter vals from 1972 to 1987 in dynamical astronomy, theoretical mechanics and celestial mechanics. These previous institutes, held in high esteem by the international community of research workers, have resulted in a series of well-received Proceedings. The 1990 Institute attracted 74 participants from 16 countries, six outside the NATO group. Fifteen series of lectures were given by invited speakers; additionally some 40 valuable presentations were made by the younger participants, most of which are included in these Proceedings. The last twenty years in particular has been a time of increasingly rapid progress in tackling long-standing and also newly-arising problems in dynamics of N-body systems, point-mass and non-point-mass, a rate of progress achieved because of correspondingly rapid developments of new computer hardware and software together with the advent of new analytical techniques. It was a time of exciting progress culminating in the ability to carry out research programmes into the evolution of the outer Solar 8 System over periods of more than 10 years and to study star cluster and galactic models in unprecedented detail.

Chaos and Forecasting

Chaos and Forecasting
Author: Howell Tong
Publsiher: World Scientific
Total Pages: 356
Release: 1995-04-26
Genre: Electronic Book
ISBN: 9789814549769

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It is now generally recognised that very simple dynamical systems can produce apparently random behaviour. In the last couple of years, attention has turned to focus on the flip side of this coin: random-looking time series (or random-looking patterns in space) may indeed be the result of very complicated processes or “real noise”, but they may equally well be produced by some very simple mechanism (a low-dimensional attractor). In either case, a long-term prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but low-dimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a two-day discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meteorology, statistics, epidemiology, earthquake science and many others. Each topic was covered by a leading expert in the field. The meeting dealt with different basic approaches to the problem of chaos and forecasting, and covered applications to nonlinear forecasting of both artificially-generated time series and real data from context in the above-mentioned diverse fields. This book marks a rather special and rare occasion on which prominent scientists from different areas converge on the same theme. It forms an informative introduction to the science of chaos, with special reference to real data. Contents:Orthogonal Projection, Embedding Dimension and Sample Size in Chaotic Time Series from a Statistical Perspective (B Cheng & H Tong)A Theory of Correlation Dimension for Stationary Time Series (C D Cutler)On Prediction and Chaos in Stochastic Systems (Q W Yao & H Tong)Locally Optimized Prediction of Nonlinear Systems: Stochastic and Deterministic (L A Smith)A Poisson Distribution for the BDS Test Statistic for Independence in a Time Series (R C L Wolff)Chaos and Nonlinear Forecastability in Economics and Finance (B LeBaron)Paradigm Change in Prediction (A S Weigend)and other papers Readership: Mathematicians, economists, statisticians and nonlinear scientists. keywords: “… useful and recommended for forecast researchers striving for a more realistic methodology that goes substantially beyond conventional statistical theory.” M A Kaboudan

Chaotic Dynamics

Chaotic Dynamics
Author: Gregory L. Baker,Jerry P. Gollub
Publsiher: Cambridge University Press
Total Pages: 282
Release: 1996
Genre: Science
ISBN: 0521471060

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The previous edition of this text was the first to provide a quantitative introduction to chaos and nonlinear dynamics at the undergraduate level. It was widely praised for the clarity of writing and for the unique and effective way in which the authors presented the basic ideas. These same qualities characterize this revised and expanded second edition. Interest in chaotic dynamics has grown explosively in recent years. Applications to practically every scientific field have had a far-reaching impact. As in the first edition, the authors present all the main features of chaotic dynamics using the damped, driven pendulum as the primary model. This second edition includes additional material on the analysis and characterization of chaotic data, and applications of chaos. This new edition of Chaotic Dynamics can be used as a text for courses on chaos for physics and engineering students at the second- and third-year level.

Nonlinear Dynamics and Statistics

Nonlinear Dynamics and Statistics
Author: Alistair I. Mees
Publsiher: Springer Science & Business Media
Total Pages: 484
Release: 2012-12-06
Genre: Business & Economics
ISBN: 9781461201779

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This book describes the state of the art in nonlinear dynamical reconstruction theory. The chapters are based upon a workshop held at the Isaac Newton Institute, Cambridge University, UK, in late 1998. The book's chapters present theory and methods topics by leading researchers in applied and theoretical nonlinear dynamics, statistics, probability, and systems theory. Features and topics: * disentangling uncertainty and error: the predictability of nonlinear systems * achieving good nonlinear models * delay reconstructions: dynamics vs. statistics * introduction to Monte Carlo Methods for Bayesian Data Analysis * latest results in extracting dynamical behavior via Markov Models * data compression, dynamics and stationarity Professionals, researchers, and advanced graduates in nonlinear dynamics, probability, optimization, and systems theory will find the book a useful resource and guide to current developments in the subject.

Coping with Chaos

Coping with Chaos
Author: Edward Ott,Tim Sauer,James A. Yorke
Publsiher: Wiley-VCH
Total Pages: 432
Release: 1994-09-27
Genre: Science
ISBN: 0471025569

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The first unified presentation of new developments in the analysis and exploitation of chaotic systems... Mathematicians have been aware of chaotic dynamics since Poincar?'s work at the turn of the century. But, as the turn of yet another century approaches, physical scientists and engineers have begun to use their understanding of chaos theory to analyze chaotic experimental time series data. Some researchers have even used the presence of chaos to achieve practical goals. To do this, they have had to work with dynamical processes for which the equations were either not known or were too complex to be useful. In other words, they have been coping with chaos. Coping with Chaos is the first book to bring together recent advances in the interpretive and practical applications of chaos, which hold great promise for broad applicability throughout the physical sciences and engineering. Together with an introduction to chaos theory, this book provides detailed reports on methods of analyzing experimental time series data from chaotic systems and studies in which the unique attributes of chaos are put to practical use. Topics discussed in this book include: * Theory of chaotic dynamics * Embedding techniques for the analysis of experimental data * Calculation of dimension and Lyapunov exponents * Determination of periodic orbits and symbolic dynamics * Prediction of chaotic time series * Noise filtering of chaotic data * Control of chaotic systems * The use of chaotic signals for communication * And more