Published September 18, 2015 | Version v1
Journal article

Quantifying the non-ergodicity of scaled Brownian motion

  • 1. Department of Physics, Shahid Beheshti University, G.C., Evin, 19839 Tehran (Iran, Islamic Republic of)
  • 2. Institute for Physics and Astronomy, University of Potsdam, D-14476 Potsdam-Golm (Germany)
  • 3. Institute for Physics, Humboldt-Universität zu Berlin, D-12489 Berlin (Germany)

Description

We examine the non-ergodic properties of scaled Brownian motion (SBM), a non-stationary stochastic process with a time dependent diffusivity of the form D ( t ) t α 1 . We compute the ergodicity breaking parameter EB in the entire range of scaling exponents α, both analytically and via extensive computer simulations of the stochastic Langevin equation. We demonstrate that in the limit of long trajectory lengths T and short lag times Δ the EB parameter as function of the scaling exponent α has no divergence at α = 1/2 and present the asymptotes for EB in different limits. We generalize the analytical and simulations results for the time averaged and ergodic properties of SBM in the presence of ageing, that is, when the observation of the system starts only a finite time span after its initiation. The approach developed here for the calculation of the higher time averaged moments of the particle displacement can be applied to derive the ergodic properties of other stochastic processes such as fractional Brownian motion. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/1751-8113/48/37/375002

Additional details

Publishing Information

Journal Title
Journal of Physics. A, Mathematical and Theoretical (Online)
Journal Volume
48
Journal Issue
37
Journal Page Range
[18 p.]
ISSN
1751-8121

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
51040474
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
BROWNIAN MOVEMENT; COMPUTERIZED SIMULATION; LANGEVIN EQUATION; STOCHASTIC PROCESSES; TIME DEPENDENCE; TRAJECTORIES
Descriptors DEC
EQUATIONS; SIMULATION