Characterisation of Physical Processes from Anomalous Diffusion Data
Guest Editors
Miguel A Garcia-March, Maciej Lewenstein, Carlo Manzo, Ralf Metzler, Gorka Muñoz-Gil, Giovanni Volpe
Journal of Physics A: Mathematical and Theoretical
URL: Special Issue on Characterisation of Physical Processes from Anomalous Diffusion Data
In many systems, stochastic transport deviates from the standard laws of Brownian motion. Determining the exponent α characterising anomalous diffusion and identifying the physical origin of this behaviour are crucial steps to understanding the nature of the systems under observation. However, the determination of these properties from the analysis of the measured trajectories is often difficult, especially when these trajectories are short, irregularly sampled, or switching between different behaviours.
Over the last years, several methods have been proposed to quantify anomalous diffusion and the underlying physical process, going beyond the classical calculation of the mean squared displacement. More recently, the advent of machine learning has produced a boost in the methods to quantify anomalous diffusion.
The AnDi challenge aims at bringing together a vibrating and multidisciplinary community of scientists working on this problem. The use of the same reference datasets will allow an unbiased assessment of the performance of methods for characterising anomalous diffusion from single trajectories. This Special Issue will report on these approaches and their performance.
The deadline for submissions will be 30th June 2021 and you can submit manuscripts through ScholarOne Manuscripts. All papers will be refereed according to the usual high standards of the journal.