A non-Fickian, particle-tracking diffusion model based on fractional Brownian motion

1997 ◽  
Vol 25 (12) ◽  
pp. 1373-1384 ◽  
Author(s):  
Paul S. Addison ◽  
Bo Qu ◽  
Alistair Nisbet ◽  
Gareth Pender
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Diffusion Model ◽  
Particle Tracking ◽  
Model Based

scholarly journals Inertia triggers nonergodicity of fractional Brownian motion

2021 ◽  
Author(s):  
Andrey Cherstvy ◽  
Wei Wang ◽  
Ralf Metzler ◽  
Igor Sokolov
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Particle Tracking ◽  
Single Particle ◽  
Single Particle Tracking ◽  
Langevin Equations ◽  
Ergodic Properties ◽  
Massive Particles ◽  
Breaking Parameter ◽  
Ergodicity Breaking

How related are the ergodic properties of the over- and underdamped Langevin equations driven by fractional Gaussian noise? We here find that for massive particles performing fractional Brownian motion (FBM) inertial effects not only destroy the stylized fact of the equivalence of the ensemble-averaged mean-squared displacement (MSD) to the time-averaged MSD (TAMSD) of overdamped or massless FBM, but also concurrently dramatically alter the values of the ergodicity breaking parameter (EB). Our theoretical results for the behavior of EB for underdamped ot massive FBM for varying particle mass m, Hurst exponent H, and trace length T are in excellent agreement with the findings of extensive stochastic computer simulations. The current results can be of interest for the experimental community employing various single-particle-tracking techniques and aiming at assessing the degree of nonergodicity for the recorded time series (studying e.g. the behavior of EB versus lag time). To infer FBM as a realizable model of anomalous diffusion for a set single-particle-tracking data when massive particles are being tracked, the EBs from the data should be compared to EBs of massive (rather than massless) FBM.

Multiple particle tracking

2018 ◽  
Author(s):  
Eric M. Furst ◽  
Todd M. Squires
Keyword(s):  
Brownian Motion ◽  
Best Practices ◽  
Particle Tracking ◽  
Heterogeneous Material ◽  
Quantitative Measurements ◽  
Particle Tracking Microrheology ◽  
Shell Models ◽  
Multiple Particle Tracking ◽  
Microscopy Data ◽  
Multiple Particle

The fundamentals and best practices of multiple particle tracking microrheology are discussed, including methods for producing video microscopy data, analyzing data to obtain mean-squared displacements and displacement correlations, and, critically, the accuracy and errors (static and dynamic) associated with particle tracking. Applications presented include two-point microrheology, methods for characterizing heterogeneous material rheology, and shell models of local (non-continuum) heterogeneity. Particle tracking has a long history. The earliest descriptions of Brownian motion relied on precise observations, and later quantitative measurements, using light microscopy.

scholarly journals A Wavelet-Based Almost-Sure Uniform Approximation of Fractional Brownian Motion with a Parallel Algorithm

2014 ◽  
Vol 51 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Dawei Hong ◽  
Shushuang Man ◽  
Jean-Camille Birget ◽  
Desmond S. Lun
Keyword(s):  
Brownian Motion ◽  
Convergence Rate ◽  
Fractional Brownian Motion ◽  
Parallel Algorithm ◽  
Uniform Approximation ◽  
Haar Wavelets ◽  
Hurst Index ◽  
Sample Paths ◽  
Vanishing Moment ◽  
Uniform Expansion

We construct a wavelet-based almost-sure uniform approximation of fractional Brownian motion (FBM) (Bt(H))_t∈[0,1] of Hurst index H ∈ (0, 1). Our results show that, by Haar wavelets which merely have one vanishing moment, an almost-sure uniform expansion of FBM for H ∈ (0, 1) can be established. The convergence rate of our approximation is derived. We also describe a parallel algorithm that generates sample paths of an FBM efficiently.

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