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4 October 2022 Characterization of anomalous diffusion with neural networks (Conference Presentation)
Stefano Bo, Aykut Argun, Falko Schmidt, Ralf Eichhorn, Giovanni Volpe
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC1220414 (2022) https://doi.org/10.1117/12.2633077
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
Many systems in biology, physics, and finance exhibit anomalous diffusion dynamics where the mean squared displacement grows with an exponent that deviates from one. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks are difficult when only few short trajectories are available, a common scenario in non-equilibrium and living systems. We show that long short-time memory (LSTM) recurrent neural networks excel at characterizing anomalous diffusion from a single short trajectory. The method we developed generalizes to experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers. We discuss the performance of the method in comparison to alternative ones in the context of the Anomalous Diffusion Challenge. In closing, we address the interpretability of the method.
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Stefano Bo, Aykut Argun, Falko Schmidt, Ralf Eichhorn, and Giovanni Volpe "Characterization of anomalous diffusion with neural networks (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC1220414 (4 October 2022); https://doi.org/10.1117/12.2633077
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KEYWORDS
Diffusion
Neural networks
Biology
Excel
Physics
Speckle
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