Wenjie Cai

Wenjie Cai, Yi Hu, Xiang Qu et al.

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.

Wenjie Cai, Gongyi Wang, Yu Zhang et al.

Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science.