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Measurement of anomalous diffusion using recurrent neural networks.

Stefano Bo1, Falko Schmidt2, Ralf Eichhorn1

  • 1Nordita, Royal Institute of Technology and Stockholm University, Roslagstullsbacken 23, SE-106 91 Stockholm, Sweden.

Physical Review. E
|September 11, 2019
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Summary

Recurrent neural networks (RNNs) efficiently characterize anomalous diffusion from short trajectories, outperforming traditional methods. RNNs also handle complex tasks like irregular sampling and intermittent diffusion systems.

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Area of Science:

  • Physics
  • Biophysics
  • Data Science

Background:

  • Anomalous diffusion describes physical and biological processes where mean squared displacement (MSD) growth deviates from linear time dependence.
  • Traditional MSD-based exponent estimation struggles with limited experimental data points.

Purpose of the Study:

  • To demonstrate recurrent neural networks (RNNs) for efficient anomalous diffusion characterization.
  • To showcase RNNs' ability to determine anomalous diffusion exponents from short, potentially irregularly sampled trajectories.
  • To apply RNNs to complex intermittent diffusion systems.

Main Methods:

  • Utilized recurrent neural networks (RNNs) to analyze trajectory data.
  • Compared RNN performance against standard MSD-based methods for exponent estimation.
  • Applied RNNs to experimental data from subdiffusive colloids and superdiffusive microswimmers.

Main Results:

  • RNNs accurately determine anomalous diffusion exponents from single, short trajectories, surpassing MSD methods with limited data.
  • RNNs successfully analyzed trajectories sampled at irregular time intervals.
  • RNNs estimated switching times and exponents in intermittent anomalous diffusion systems.

Conclusions:

  • RNNs offer a powerful and versatile tool for analyzing anomalous diffusion across various physical and biological systems.
  • The proposed RNN approach enhances the analysis of complex diffusion dynamics, especially under experimental constraints.