Journal article
Stefano Bo, Falko Schmidt, Ralf Eichhorn, Giovanni Volpe
Physical Review E, vol. 100(1), 2019 Jun 17
Physical Review E, vol. 100(1), 2019 Jun 17
View PDF
Link to article
Cite
Cite
APA
Click to copy
Bo, S., Schmidt, F., Eichhorn, R., & Volpe, G. (2019). Measurement of anomalous diffusion using recurrent neural networks. Physical Review E, 100(1).
Chicago/Turabian
Click to copy
Bo, Stefano, Falko Schmidt, Ralf Eichhorn, and Giovanni Volpe. “Measurement of Anomalous Diffusion Using Recurrent Neural Networks.” Physical Review E 100, no. 1 (June 17, 2019).
MLA
Click to copy
Bo, Stefano, et al. “Measurement of Anomalous Diffusion Using Recurrent Neural Networks.” Physical Review E, vol. 100, no. 1, June 2019.
BibTeX Click to copy
@article{stefano2019a,
title = {Measurement of anomalous diffusion using recurrent neural networks.},
year = {2019},
month = jun,
day = {17},
issue = {1},
journal = {Physical Review E},
volume = {100},
author = {Bo, Stefano and Schmidt, Falko and Eichhorn, Ralf and Volpe, Giovanni},
month_numeric = {6}
}
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.
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.
