The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Ying-cheng Lai - One of the best experts on this subject based on the ideXlab platform.

  • chaos in dirac electron optics emergence of a relativistic quantum chimera
    Physical Review Letters, 2018
    Co-Authors: Guanglei Wang, Liang Huang, Ying-cheng Lai
    Abstract:

    We uncover a remarkable quantum scattering phenomenon in two-dimensional Dirac material Systems where the manifestations of both classically integrable and chaotic dynamics emerge simultaneously and are electrically controllable. The distinct relativistic quantum fingerprints associated with different electron spin states are due to a physical mechanism analogous to a chiroptical effect in the presence of degeneracy breaking. The phenomenon mimics a chimera state in classical Complex Dynamical Systems but here in a relativistic quantum setting-henceforth the term "Dirac quantum chimera," associated with which are physical phenomena with potentially significant applications such as enhancement of spin polarization, unusual coexisting quasibound states for distinct spin configurations, and spin selective caustics. Experimental observations of these phenomena are possible through, e.g., optical realizations of ballistic Dirac fermion Systems.

  • detection of time delays and directional interactions based on time series from Complex Dynamical Systems
    Physical Review E, 2017
    Co-Authors: Siyang Leng, Ying-cheng Lai, Jurgen Kurths, Chenyang Tao, Xiong Ying, Wei Lin
    Abstract:

    Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in Complex Dynamical Systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting Dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in Complex Systems arising from a broad range of disciplines.

  • data based identification and prediction of nonlinear and Complex Dynamical Systems
    arXiv: Data Analysis Statistics and Probability, 2017
    Co-Authors: Ying-cheng Lai, Wen-xu Wang, Celso Grebogi
    Abstract:

    The problem of reconstructing nonlinear and Complex Dynamical Systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. In this paper, we review the recent advances in this forefront and rapidly evolving field, aiming to cover topics such as compressive sensing (a novel optimization paradigm for sparse-signal reconstruction), noised-induced Dynamical mapping, perturbations, reverse engineering, synchronization, inner composition alignment, global silencing, Granger Causality and alternative optimization algorithms. Often, these rely on various concepts from statistical and nonlinear physics such as phase transitions, bifurcation, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of Complex Dynamical Systems ranging from gene regulatory Systems to social networks towards the ultimate goal of controlling such Systems. Despite recent progress, many challenges remain. A purpose of this Review is then to point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this interdisciplinary field.

  • Data based identification and prediction of nonlinear and Complex Dynamical Systems
    Physics Reports, 2016
    Co-Authors: Wen-xu Wang, Ying-cheng Lai, Celso Grebogi
    Abstract:

    The problem of reconstructing nonlinear and Complex Dynamical Systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional Dynamical Systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the Systems of interest are Complex and high dimensional with many interacting components. A Complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and Complex Systems identification aims at inferring, from data, the mathematical equations that govern the Dynamical evolution and the Complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The “inverse” problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary Complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear Dynamical Systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and Complex Systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and Complex networked Systems. The problems include identification of chaotic Systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear Systems, reconstruction of Complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of Complex geospatial networks and nodal positioning, and reconstruction of Complex spreading networks with binary data. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced Dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of Complex nonlinear Dynamical networks are discussed. The methods outlined in this Review are principled on various concepts in Complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of Complex Dynamical Systems ranging from gene regulatory Systems to social networks toward the ultimate goal of controlling such Systems.

  • robustness of chimera states in Complex Dynamical Systems
    Scientific Reports, 2013
    Co-Authors: Nan Yao, Ying-cheng Lai, Zigang Huang, Zhigang Zheng
    Abstract:

    The remarkable phenomenon of chimera state in Systems of non-locally coupled, identical oscillators has attracted a great deal of recent theoretical and experimental interests. In such a state, different groups of oscillators can exhibit characteristically distinct types of Dynamical behaviors, in spite of identity of the oscillators. But how robust are chimera states against random perturbations to the structure of the underlying network? We address this fundamental issue by studying the effects of random removal of links on the probability for chimera states. Using direct numerical calculations and two independent theoretical approaches, we find that the likelihood of chimera state decreases with the probability of random-link removal. A striking finding is that, even when a large number of links are removed so that chimera states are deemed not possible, in the state space there are generally both coherent and incoherent regions. The regime of chimera state is a particular case in which the oscillators in the coherent region happen to be synchronized or phase-locked.

Jurgen Kurths - One of the best experts on this subject based on the ideXlab platform.

  • detection of time delays and directional interactions based on time series from Complex Dynamical Systems
    Physical Review E, 2017
    Co-Authors: Siyang Leng, Ying-cheng Lai, Jurgen Kurths, Chenyang Tao, Xiong Ying, Wei Lin
    Abstract:

    Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in Complex Dynamical Systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting Dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in Complex Systems arising from a broad range of disciplines.

  • stability threshold approach for Complex Dynamical Systems
    New Journal of Physics, 2015
    Co-Authors: Vladimir Klinshov, Vladimir I Nekorkin, Jurgen Kurths
    Abstract:

    Acknowledgments This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP, and supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS). The first author thanks Dr Roman Ovsyannikov for valuable discussions regarding estimation of the mistake probability.

  • stability threshold approach for Complex Dynamical Systems
    arXiv: Chaotic Dynamics, 2015
    Co-Authors: Vladimir Klinshov, Vladimir I Nekorkin, Jurgen Kurths
    Abstract:

    A new measure to characterize stability of Complex Dynamical Systems against large perturbation is suggested, the stability threshold (ST). It quantifies the magnitude of the weakest perturbation capable to disrupt the system and switch it to an undesired Dynamical regime. In the phase space, the stability threshold corresponds to the "thinnest site" of the attraction basin and therefore indicates the most "dangerous" direction of perturbations. We introduce a computational algorithm for quantification of the stability threshold and demonstrate that the suggested approach is effective and provides important insights. The generality of the obtained results defines their vast potential for application in such fields as engineering, neuroscience, power grids, Earth science and many others where robustness of Complex Systems is studied.

Yunjian Peng - One of the best experts on this subject based on the ideXlab platform.

Celso Grebogi - One of the best experts on this subject based on the ideXlab platform.

  • data based identification and prediction of nonlinear and Complex Dynamical Systems
    arXiv: Data Analysis Statistics and Probability, 2017
    Co-Authors: Ying-cheng Lai, Wen-xu Wang, Celso Grebogi
    Abstract:

    The problem of reconstructing nonlinear and Complex Dynamical Systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. In this paper, we review the recent advances in this forefront and rapidly evolving field, aiming to cover topics such as compressive sensing (a novel optimization paradigm for sparse-signal reconstruction), noised-induced Dynamical mapping, perturbations, reverse engineering, synchronization, inner composition alignment, global silencing, Granger Causality and alternative optimization algorithms. Often, these rely on various concepts from statistical and nonlinear physics such as phase transitions, bifurcation, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of Complex Dynamical Systems ranging from gene regulatory Systems to social networks towards the ultimate goal of controlling such Systems. Despite recent progress, many challenges remain. A purpose of this Review is then to point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this interdisciplinary field.

  • Data based identification and prediction of nonlinear and Complex Dynamical Systems
    Physics Reports, 2016
    Co-Authors: Wen-xu Wang, Ying-cheng Lai, Celso Grebogi
    Abstract:

    The problem of reconstructing nonlinear and Complex Dynamical Systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional Dynamical Systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the Systems of interest are Complex and high dimensional with many interacting components. A Complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and Complex Systems identification aims at inferring, from data, the mathematical equations that govern the Dynamical evolution and the Complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The “inverse” problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary Complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear Dynamical Systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and Complex Systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and Complex networked Systems. The problems include identification of chaotic Systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear Systems, reconstruction of Complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of Complex geospatial networks and nodal positioning, and reconstruction of Complex spreading networks with binary data. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced Dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of Complex nonlinear Dynamical networks are discussed. The methods outlined in this Review are principled on various concepts in Complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of Complex Dynamical Systems ranging from gene regulatory Systems to social networks toward the ultimate goal of controlling such Systems.

John Harlim - One of the best experts on this subject based on the ideXlab platform.

  • the role of additive and multiplicative noise in filtering Complex Dynamical Systems
    Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2013
    Co-Authors: Georg A Gottwald, John Harlim
    Abstract:

    Covariance inflation is an ad hoc treatment that is widely used in practical real-time data assimilation algorithms to mitigate covariance underestimation owing to model errors, nonlinearity, or/an...

  • information flow between subspaces of Complex Dynamical Systems
    Proceedings of the National Academy of Sciences of the United States of America, 2007
    Co-Authors: Andrew J Majda, John Harlim
    Abstract:

    The quantification of information flow between subspaces in ensemble predictions for Complex Dynamical Systems is an important practical topic, for example, in weather prediction and climate change projections. Although information transfer between Dynamical system components is an established concept for nonlinear multivariate time series, the specific nature of the nonlinear dynamics generating the observed flow of information is ignored in such statistical analysis. Here, a general mathematical theory for information flow between subspaces in ensemble predictions of a Dynamical system is developed, which accounts for the specific underlying dynamics. The results below also include potentially useful approximation strategies for practical implementation in Dynamical Systems with many degrees of freedom. Specific elementary examples are developed here with both stable and unstable dynamics to both illustrate facets of the theory and to test Monte Carlo solution strategies.