Preface: Characterisation of Physical Processes from Anomalous Diffusion Data

Abstract

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. The list of articles included in the special issue can be accessed at https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data .

arXiv commentPreface to the Special Issue "Characterisation of Physical Processes from Anomalous Diffusion Data", Journal of Physics A: Mathematical and Theoretical https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data

References in corpus (19)

• Machine learning method for single trajectory characterization
• Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach
• Spectral content of a single non-Brownian trajectory
• Measurement of Anomalous Diffusion Using Recurrent Neural Networks
• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
• Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
• Learning physical properties of anomalous random walks using graph neural networks
• Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis
• Boosting the performance of anomalous diffusion classifiers with the proper choice of features
• Bayesian inference of scaled versus fractional Brownian motion
• Characterization of anomalous diffusion through convolutional transformers
• Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
• AnDi: The Anomalous Diffusion Challenge
• Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)
• Unsupervised learning of anomalous diffusion data
• WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)
• Anomalous diffusion originated by two Markovian hopping-trap mechanisms
• Brownian non-Gaussian diffusion of self-avoiding walks

Cited by in corpus (3)

• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Semantic Segmentation of Anomalous Diffusion Using Deep Convolutional Networks
• Machine Learning Analysis of Anomalous Diffusion