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Measurement of anomalous diffusion using recurrent neural networks
Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
Number of Authors: 42019 (English)In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 100, no 1, article id 010102Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 100, no 1, article id 010102
National Category
Physical Sciences Mathematics
Identifiers
URN: urn:nbn:se:su:diva-171662DOI: 10.1103/PhysRevE.100.010102ISI: 000476695300001OAI: oai:DiVA.org:su-171662DiVA, id: diva2:1344616
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-21Bibliographically approved

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Bo, StefanoEichhorn, RalfVolpe, Giovanni
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