mlsanityData to 7 Oct 2026

Machine Learning Analysis of Anomalous Diffusion

arXiv 2412.01393 ↗First posted 2 Dec 2024cs.LG cond-mat.soft physics.bio-phcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 182 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

26 of 182 references.

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14 more
  1. 2022Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motioncited by 15
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Reinforcement Learning for Active Mattercited by 4
  2. 2025ALT: A Python Package for Lightweight Feature Representation in Time Series Classificationcited by 0
  3. 2025Data-Efficient Learning of Anomalous Diffusion with Wavelet Representations: Enabling Direct Learning from Experimental Trajectoriescited by 0