AnDi: The Anomalous Diffusion Challenge

Abstract

The deviation from pure Brownian motion generally referred to as anomalous diffusion has received large attention in the scientific literature to describe many physical scenarios. Several methods, based on classical statistics and machine learning approaches, have been developed to characterize anomalous diffusion from experimental data, which are usually acquired as particle trajectories. With the aim to assess and compare the available methods to characterize anomalous diffusion, we have organized the Anomalous Diffusion (AnDi) Challenge (\url{http://www.andi-challenge.org/}). Specifically, the AnDi Challenge will address three different aspects of anomalous diffusion characterization, namely: (i) Inference of the anomalous diffusion exponent. (ii) Identification of the underlying diffusion model. (iii) Segmentation of trajectories. Each problem includes sub-tasks for different number of dimensions (1D, 2D and 3D). In order to compare the various methods, we have developed a dedicated open-source framework for the simulation of the anomalous diffusion trajectories that are used for the training and test datasets. The challenge was launched on March 1, 2020, and consists of three phases. Currently, the participation to the first phase is open. Submissions will be automatically evaluated and the performance of the top-scoring methods will be thoroughly analyzed and compared in an upcoming article.

arXiv comment6 pages, 2 figure - AnDi Challenge webpage: http://www.andi-challenge.org

Cited by in corpus (10)

• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Learning physical properties of anomalous random walks using graph neural networks
• Boosting the performance of anomalous diffusion classifiers with the proper choice of features
• Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)
• Anomalous diffusion in nonlinear transformations of the noisy voter model
• Variational inference of fractional Brownian motion with linear computational complexity
• Machine Learning Analysis of Anomalous Diffusion
• Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
• Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates
• Bayesian inference of Lévy walks via hidden Markov models