标题： 机器学习分析异常扩散 显示英文标题

标题： Machine Learning Analysis of Anomalous Diffusion

摘要： 机器学习的迅速发展使其在异常扩散分析中的应用变得必不可少且不可避免。 本综述系统地介绍了机器学习技术在增强异常扩散分析中的整合，重点集中在两个关键方面：通过机器学习进行单轨迹表征以及异常扩散的表示学习。 我们广泛比较了各种机器学习方法，包括经典的机器学习和深度学习，用于扩散参数推断和轨迹分割。 此外，还强调了诸如异常扩散挑战之类的平台，这些平台作为评估这些方法的基准。 另一方面，我们概述了三种主要的异常扩散表示策略：预定义特征的组合、神经网络倒数第二层的特征向量以及自编码器的潜在表示，并分析了它们在不同场景下的适用性。 这项研究为未来的研究铺平了道路，提供了有价值的见解，可以进一步丰富异常扩散的研究，并推动人工智能在统计物理和生物物理中的应用。 显示英文摘要

摘要： 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.