Can the original equations of a dynamical system be retrieved from observational time series?

The aim of the present work is to investigate the possibility to retrieve the original sets of dynamical equations directly from observational time series when all the system variables are observed. Time series are generated from chosen dynamical systems, and the global modeling technique is applied to obtain optimal models of parsimonious structure from these time series. The obtained models are then compared to the original equations to investigate if the original equations can be retrieved. Twenty-seven systems are considered in the study. The Rössler system is first used to illustrate the procedure and then to test the robustness of the approach under various conditions, varying the initial conditions, time series length, dynamical regimes, subsampling (and resampling), measurement noise, and dynamical perturbations. The other 26 systems (four rational ones included) of various algebraic structures, sizes, and dimensions are then considered to investigate the generality of the approach.

[1]  Luis A. Aguirre,et al.  Modeling Nonlinear Dynamics and Chaos: A Review , 2009 .

[2]  L. A. Aguirre,et al.  Equivalence of non-linear model structures based on Pareto uncertainty , 2015 .

[3]  Sundarapandian Vaidyanathan,et al.  A 5-D hyperchaotic Rikitake dynamo system with hidden attractors , 2015 .

[4]  Peter Young,et al.  Parameter estimation for continuous-time models - A survey , 1979, Autom..

[5]  Luis A Aguirre,et al.  Global models from the Canadian lynx cycles as a direct evidence for chaos in real ecosystems , 2007, Journal of mathematical biology.

[6]  Sylvain Mangiarotti,et al.  Using global modeling to unveil hidden couplings in small network motifs. , 2018, Chaos.

[7]  Claudia Lainscsek,et al.  A class of Lorenz-like systems. , 2012, Chaos.

[8]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[9]  I. J. Leontaritis,et al.  Parameter Estimation Techniques for Nonlinear Systems , 1982 .

[10]  Letellier,et al.  Global vector field reconstruction from a chaotic experimental signal in copper electrodissolution. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  Yann Kerr,et al.  Can the global modeling technique be used for crop classification , 2018 .

[12]  S. Nosé A molecular dynamics method for simulations in the canonical ensemble , 1984 .

[13]  C Letellier,et al.  Ansatz library for global modeling with a structure selection. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  O. Rössler An equation for continuous chaos , 1976 .

[15]  Sylvain Mangiarotti,et al.  Topological characterization versus synchronization for assessing (or not) dynamical equivalence. , 2018, Chaos.

[16]  Christophe Letellier,et al.  Frequently asked questions about global modeling. , 2009, Chaos.

[17]  S. Mangiarotti Low dimensional chaotic models for the plague epidemic in Bombay (1896–1911) , 2015 .

[18]  Nonlinear conductivity and entropy in the two-body Boltzmann gas , 1986 .

[19]  J. Sprott,et al.  Some simple chaotic flows. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  J. Malasoma Countable infinite sequence of attractors' families for the simplest known equivariant chaotic flow , 2002 .

[21]  T. Rikitake,et al.  Oscillations of a system of disk dynamos , 1958, Mathematical Proceedings of the Cambridge Philosophical Society.

[22]  Ganti Prasada Rao,et al.  Identification of Continuous Dynamical Systems , 1983 .

[23]  Amit Sharma,et al.  The dynamic of plankton-nutrient interaction with delay , 2014, Appl. Math. Comput..

[24]  Dequan Li,et al.  A three-scroll chaotic attractor , 2008 .

[25]  Claudia Lainscsek,et al.  Nonuniqueness of global modeling and time scaling. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Sylvain Mangiarotti,et al.  Global modeling of aggregated and associated chaotic dynamics , 2016 .

[27]  Required criteria for recognizing new types of chaos: application to the "cord" attractor. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Robert Shaw Strange Attractors, Chaotic Behavior, and Information Flow , 1981 .

[29]  L. Jarlan,et al.  Polynomial search and global modeling: Two algorithms for modeling chaos. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Christophe Letellier,et al.  Global modeling of the Rössler system from the /z-variable , 2003 .

[31]  Stephen P. Banks,et al.  Chaos in a Three-Dimensional Cancer Model , 2010, Int. J. Bifurc. Chaos.

[32]  M. Huc,et al.  A chaotic model for the epidemic of Ebola virus disease in West Africa (2013-2016). , 2016, Chaos.

[33]  Stephen A. Billings,et al.  RETRIEVING DYNAMICAL INVARIANTS FROM CHAOTIC DATA USING NARMAX MODELS , 1995 .

[34]  Letellier,et al.  Global vector-field reconstruction by using a multivariate polynomial L2 approximation on nets. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[35]  Alberto Tesi,et al.  Harmonic balance methods for the analysis of chaotic dynamics in nonlinear systems , 1992, Autom..

[36]  Two chaotic global models for cereal crops cycles observed from satellite in northern Morocco. , 2014, Chaos.