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Small networks of chaotic units which are coupled by their time-delayed variables, are investigated. In spite of the time delay, the units can synchronize isochronally, i.e. without time shift. Moreover, networks can not only synchronize…

Chaotic Dynamics · Physics 2009-11-13 Johannes Kestler , Evi Kopelowitz , Ido Kanter , Wolfgang Kinzel

The problem of Turing pattern formation has attracted much attention in nonlinear science as well as physics, chemistry and biology. So far all Turing patterns have been observed in stationary and oscillatory media only. In this letter we…

Pattern Formation and Solitons · Physics 2007-05-23 Jinghua Xiao , Junzhong Yang , Gang Hu

The paper describes the application of some numerical techniques to analyze and to characterize the observed dynamical behaviour of fluidized bed systems. The preliminary results showed clearly that the dynamics of the considered process…

comp-gas · Physics 2016-08-31 E. Caputo , S. Sello , ; V. Marcolongo

Time series data that are not measured at regular intervals are commonly discretized as a preprocessing step. For example, data about customer arrival times might be simplified by summing the number of arrivals within hourly intervals,…

Machine Learning · Statistics 2018-10-09 Peter Schulam , Suchi Saria

We examine the regenerative cutting process by using a single degree of freedom non-smooth model with a friction component and a time delay term. Instead of the standard Lyapunov exponent calculations, we propose a statistical 0-1 test…

Chaotic Dynamics · Physics 2012-01-25 Grzegorz Litak , Sven Schubert , Guenter Radons

Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based…

Machine Learning · Computer Science 2021-06-17 Hagai Rappeport , Irit Levin Reisman , Naftali Tishby , Nathalie Q. Balaban

Before we apply nonlinear techniques, for example those inspired by chaos theory, to dynamical phenomena occurring in nature, it is necessary to first ask if the use of such advanced techniques is justified "by the data". While many…

chao-dyn · Physics 2009-10-31 Thomas Schreiber , Andreas Schmitz

The paper deals with the theoretical analysis of a logistic system composed of at least two elements with distributed parameters. It has been shown that such a system may generate specific oscillations in spite of the fact that the…

Chaotic Dynamics · Physics 2026-02-10 Marek Berezowski , Artur Grabski

This paper proves that shadowing solutions can be almost surely nonphysical. This finding invalidates the argument that small perturbations in a chaotic system can only have a small impact on its statistical behavior. This theoretical…

Chaotic Dynamics · Physics 2021-06-02 Nisha Chandramoorthy , Qiqi Wang

A new approach is proposed to the integrated analysis of the time structure of synchronization of multidimensional chaotic systems. The method allows one to diagnose and quantitatively evaluate the intermittency characteristics during…

Chaotic Dynamics · Physics 2015-07-02 A. V. Makarenko

The information bleaching refers to any physical process that removes quantum information from the initial state of the physical system. The no-hiding theorem proves that if information is lost from the initial system, then it cannot remain…

Quantum Physics · Physics 2022-06-23 Abhay Srivastav , Arun Kumar Pati

The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of…

Machine Learning · Statistics 2020-03-31 Marc Bocquet , Julien Brajard , Alberto Carrassi , Laurent Bertino

The CLEAN algorithm, widely used in radio interferometry for the deconvolution of radio images, performs well only if the raw radio image (dirty image) is, to good approximation, a simple convolution between the instrumental point-spread…

Instrumentation and Methods for Astrophysics · Physics 2015-05-28 I. M. Stewart , D. M. Fenech , T. W. B. Muxlow

Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical system is a venerable task, permitting provably efficient solutions only in special cases. This work…

Machine Learning · Computer Science 2020-03-03 Chloe Ching-Yun Hsu , Michaela Hardt , Moritz Hardt

The paper considers causal smoothing of the real sequences, i.e.,discrete time processes in a deterministic setting. A family of causal linear time-invariant filters is suggested. These filters approximate the gain decay for some non-causal…

Optimization and Control · Mathematics 2015-07-07 Nikolai Dokuchaev

Reliable prediction of large chaotic sytems in the short to middle time range is of interest in a number of fields, including climate, ecology, seismology, and economics. In this paper, results from chaos theory, and statistical theory are…

Applications · Statistics 2013-12-17 M. LuValle

Nonlinear time series analysis is becoming a more and more reliable tool for the study of complicated dynamics from measurements. The concept of low-dimensional chaos has proven to be fruitful in the understanding of many complex phenomena…

chao-dyn · Physics 2009-10-31 Rainer Hegger , Holger Kantz , Thomas Schreiber

Strong nonlinear effects combined with diffusive coupling may give rise to unpredictable evolution in spatially extended deterministic dynamical systems even in the presence of a fully negative spectrum of Lyapunov exponents. This regime,…

Chaotic Dynamics · Physics 2009-11-07 F. Ginelli , R. Livi , A. Politi

Improvement in time resolution sometimes introduces short-range random noises into temporal data sequences. These noises affect the results of power-spectrum analyses and the Detrended Fluctuation Analysis (DFA). The DFA is one of useful…

Data Analysis, Statistics and Probability · Physics 2009-02-05 Shin-ichi Tadaki

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking…

Computer Vision and Pattern Recognition · Computer Science 2018-10-30 Jaakko Lehtinen , Jacob Munkberg , Jon Hasselgren , Samuli Laine , Tero Karras , Miika Aittala , Timo Aila