Classification and measurement of anomalous diffusion using recurrent neural networks

Stefano Bo; Aykut Argun; Falko Schmidt; Ralf Eichhorn; Giovanni Volpe

doi:10.1117/12.2592679

1 August 2021 Classification and measurement of anomalous diffusion using recurrent neural networks

Stefano Bo, Aykut Argun, Falko Schmidt, Ralf Eichhorn, Giovanni Volpe

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180419 (2021) https://doi.org/10.1117/12.2592679
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement with time has an exponent different from one and can be due to different mechanisms. We show that recurrent neural networks (RNNs) efficiently characterize anomalous diffusion by identifying the mechanism causing it and determining the anomalous exponent from a single short trajectory. This method outperforms standard techniques and advanced ones when the available data points are limited, as is often the case in experiments. Furthermore, 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 measuring intermittent systems that switch between different kinds of anomalous diffusion. The method is validated on experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers.

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Citation Download Citation

Stefano Bo, Aykut Argun, Falko Schmidt, Ralf Eichhorn, and Giovanni Volpe "Classification and measurement of anomalous diffusion using recurrent neural networks", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180419 (1 August 2021); https://doi.org/10.1117/12.2592679

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KEYWORDS

Diffusion

Neural networks

Speckle

Statistical analysis

Switches

Switching

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