Deviations from the standard laws of Brownian motion, the linear time
dependence of the mean squared displacement and the Gaussian probability
density function, are quite commonly observed in an abundance of systems
[1]. The physical mechanisms for these anomalies are non-universal,
prompting the need for different stochastic models along with their
identification from measured time series of dynamic motion. The model
classification and parameter regression of anomalous diffusion can be
successfully achieved by machine-learning tools such as Bayesian Deep
Learning [2], which will be introduced along with a brief summary of the
two recent AnDi (Anomalous Diffusion) Challenges [3,4].
The talk will focus on long-range dependent stochastic motion, identified
in a large range of systems [5]. In particular, it will be discussed how
to generalise such models to situations, in which the observed probability
density is non-Gaussian, or when the processes display scaling exponents
varying in time or space. Diffusion models with stochastically [6,7] and
deterministically [8] varying diffusion coefficients and scaling exponents
will be introduced. Applications to experimental data will be discussed.
References:
[1] E. Barkai, Y. Garini, and R. Metzler, Strange kinetics of single molecules
in Living Cells, Phys. Today 65(8), 29 (2012); D. Krapf and R. Metzler, Strange
interfacial molecular dynamics, Phys. Today 72(9), 48 (2019).
[2] H. Seckler and R. Metzler, Bayesian deep learning for error estimation
in the analysis of anomalous diffusion, Nature Commun. 13, 6717 (2022).
[3] G. Munoz-Gil Objective comparison of methods to decode anomalous diffusion,
Nature Commun. 12, 6253 (2021)
[4] G. Munoz-Gilet al, Quantitative evaluation of methods to analyze motion
changes in single-particle experiments, Nature Commun. 16, 6749 (2025).
[5] O. Vilk, E. Aghion, T. Avgar, C. Beta, O. Nagel, A. Sabri, R. Sarfati,
D. K. Schwartz, M. Weiss, D. Krapf, R. Nathan, R. Metzler, and M. Assaf,
Unravelling the origins of anomalous diffusion: from molecules to migrating
storks, Phys. Rev. Res. 4, 033055 (2022).
[6] M. Balcerek, S. Thapa, K. Burnecki, H. Kantz, R. Metzler, A. Wylmanska,
and A. Chechkin, Multifractional Brownian motion with telegraphic,
stochastically varying exponent, Phys. Rev. Lett. 134, 197101 (2025).
[7] W. Wang, F. Seno, I. M. Sokolov, A. V. Chechkin, and R. Metzler,
Unexpected crossovers in correlated random-diffusivity processes, New J.
Phys. 22, 083041 (2020).
[8] W. Wang, M. Balcerek, K. Burnecki, A. V. Chechkin, S. Janusonis, J
Slezak, T. Vojta, A. Wylmanska, and R. Metzler, Memory-multi-fractional
Brownian motion with continuous correlations, Phys. Rev. Res. 5, L032025
(2023).