Capturing deviation from ergodicity at different scales

Abstract We address here the issue of quantifying the extent to which a given dynamical system falls short of being ergodic and introduce a new multiscale technique which we call the “ergodicity defect”. Our approach is aimed at capturing both deviation from ergodicity and its dependence on scale. The method uses ergodic theory of dynamical systems and applies harmonic analysis, in particular the scaling analysis is motivated by wavelet theory. We base the definition of the ergodicity defect on the Birkhoff characterization. We systematically exploit the role of the observation function by using characteristic functions arising from a dyadic equipartition of the phase space. This allows us to view the dependence of the defect on scale. In order to build intuition, we consider the defect for specific examples with known dynamic properties and we are able to explicitly compute the defect for some of these simple examples. We focus on three distinctive cases of the dependence of the defect on scale: (1) a defect value that increases as the scale becomes finer, (2) a defect value decreasing with scale and (3) a defect value independent of scale, which occurs for instance when a map is ergodic. We explain the information contained in these three scenarios. We see more complicated behavior with an example which has invariant subsets at various scales.

[1]  I. Mezić,et al.  Patchiness: A New Diagnostic for Lagrangian Trajectory Analysis in Time-Dependent Fluid Flows , 1998 .

[2]  Andrzej Banaszuk,et al.  A Backstepping Controller for a Nonlinear Partial Differential Equation Model of Compression System , 1998 .

[3]  Karl Petersen Ergodic Theory , 1983 .

[4]  Christopher K. R. T. Jones,et al.  The Loop Current and adjacent rings delineated by Lagrangian analysis of the near-surface flow , 2002 .

[5]  J. Ottino The Kinematics of Mixing: Stretching, Chaos, and Transport , 1989 .

[6]  A. G. Kachurovskii,et al.  The rate of convergence in ergodic theorems , 1996 .

[7]  Igor Mezic,et al.  On the geometrical and statistical properties of dynamical systems : theory and applications , 1994 .

[8]  I. Mezić,et al.  Ergodic theory and experimental visualization of invariant sets in chaotically advected flows , 2002 .

[9]  George Haller,et al.  The geometry and statistics of mixing in aperiodic flows , 1999 .

[10]  J. Craggs Applied Mathematical Sciences , 1973 .

[11]  Paul F. Tupper,et al.  Ergodicity and the Numerical Simulation of Hamiltonian Systems , 2005, SIAM J. Appl. Dyn. Syst..

[12]  R. Mañé,et al.  Ergodic Theory and Differentiable Dynamics , 1986 .

[13]  V. I. Arnolʹd,et al.  Ergodic problems of classical mechanics , 1968 .

[14]  B. Torrésani,et al.  Wavelets: Mathematics and Applications , 1994 .

[15]  G. Birkhoff Proof of the Ergodic Theorem , 1931, Proceedings of the National Academy of Sciences.

[16]  A. Kirwan,et al.  Assessing coherent feature kinematics in ocean models , 2004 .

[17]  Matthew Nicol,et al.  Acceleration of one-dimensional mixing by discontinuous mappings , 2002 .

[18]  I. Mezić,et al.  A multiscale measure for mixing , 2005 .

[19]  B. Chirikov A universal instability of many-dimensional oscillator systems , 1979 .

[20]  I. Mezić,et al.  A multiscale measure for mixing and its applications , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).