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Found 4 papers, 3 papers with code
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena.
Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge. org ) and published in Journal of Physics A: Mathematical and Theoretical.
Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories
The study of the dynamics of natural and artificial systems has provided several examples of deviations from Brownian behavior, generally defined as anomalous diffusion.
Machine learning method for single trajectory characterization
In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy.
