Machine Learning Analysis of Anomalous Diffusion

TL;DR

This review explores how machine learning techniques, including deep learning and representation learning, are transforming the analysis of anomalous diffusion in physics and biophysics, highlighting methods, benchmarks, and future directions.

Contribution

It systematically reviews machine learning applications in anomalous diffusion analysis, comparing methods and proposing new representation strategies for improved inference.

Findings

Comparison of classical and deep learning methods for diffusion inference

Evaluation of benchmark platforms like the Anomalous Diffusion Challenge

Analysis of three primary representation strategies for anomalous diffusion

Abstract

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the Anomalous Diffusion Challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.