WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)

Abstract

Anomalous diffusion, which shows a deviation of transport dynamics from the framework of standard Brownian motion, is involved in the evolution of various physical, chemical, biological, and economic systems. The study of such random processes is of fundamental importance in unveiling the physical properties of random walkers and complex systems. However, classical methods to characterize anomalous diffusion are often disqualified for individual short trajectories, leading to the launch of the Anomalous Diffusion (AnDi) Challenge. This challenge aims at objectively assessing and comparing new approaches for single trajectory characterization, with respect to three different aspects: the inference of the anomalous diffusion exponent; the classification of the diffusion model; and the segmentation of trajectories. In this article, to address the inference and classification tasks in the challenge, we develop a WaveNet-based deep neural network (WADNet) by combining a modified WaveNet encoder with long short-term memory networks, without any prior knowledge of anomalous diffusion. As the performance of our model has surpassed the current 1st places in the challenge leaderboard on both two tasks for all dimensions (6 subtasks), WADNet could be the part of state-of-the-art techniques to decode the AnDi database. Our method presents a benchmark for future research, and could accelerate the development of a versatile tool for the characterization of anomalous diffusion.

18 pages, 9 figures

References in corpus (6)

• Understanding individual human mobility patterns
• Statistical physics of social dynamics
• Anomalous transport in the crowded world of biological cells
• Fluctuations and Rheology in Active Bacterial Suspensions
• Measurement of Anomalous Diffusion Using Recurrent Neural Networks
• Scaling relations in the diffusive infiltration in fractals

Cited by in corpus (9)

• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Characterization of anomalous diffusion through convolutional transformers
• Unsupervised learning of anomalous diffusion data
• Semantic Segmentation of Anomalous Diffusion Using Deep Convolutional Networks
• 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
• Recurrent neural network analysis of single trajectories switching between anomalous diffusion states