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Measurement of anomalous diffusion using recurrent neural networks

Journal article
Authors S. Bo
Falko Schmidt
R. Eichhorn
Giovanni Volpe
Published in Physical Review E
Volume 100
Issue 1
ISSN 2470-0045
Publication year 2019
Published at Department of Physics (GU)
Language en
Links dx.doi.org/10.1103/PhysRevE.100.010...
Keywords Physics
Subject categories Physical Sciences

Abstract

Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement (MSD) with time has an exponent different from one. We show that recurrent neural networks (RNNs) can efficiently characterize anomalous diffusion by determining the exponent from a single short trajectory, outperforming the standard estimation based on the MSD when the available data points are limited, as is often the case in experiments. Furthermore, the RNNs can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and estimating the switching time and anomalous diffusion exponents of an intermittent system that switches between different kinds of anomalous diffusion. We validate our method on experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers.

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