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The characterization of diffusion processes is a keystone in ourunderstanding of a variety of physical phenomena. Many of these deviate fromBrownian motion, giving rise to anomalous diffusion. Various theoretical modelsexists nowadays to describe such processes, but their application toexperimental setups is often challenging, due to the stochastic nature of thephenomena and the difficulty to harness reliable data. The latter oftenconsists on short and noisy trajectories, which are hard to characterize withusual statistical approaches. In recent years, we have witnessed an impressiveeffort to bridge theory and experiments by means of supervised machine learningtechniques, with astonishing results. In this work, we explore the use ofunsupervised methods in anomalous diffusion data. We show that the maindiffusion characteristics can be learnt without the need of any labelling ofthe data. We use such method to discriminate between anomalous diffusion modelsand extract their physical parameters. Moreover, we explore the feasibility offinding novel types of diffusion, in this case represented by compositions ofexisting diffusion models. At last, we showcase the use of the method inexperimental data and demonstrate its advantages for cases where supervisedlearning is not applicable.

Unsupervised learning of anomalous diffusion data