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arXiv 2108.03411cond-mat.stat-mechcs.LGphysics.bio-phphysics.data-an
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反常扩散数据的无监督学习

Unsupervised learning of anomalous diffusion data

ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology(ICFO 光子科学研究所,巴塞罗那科学技术学院);ICREA(加泰罗尼亚研究与高级研究所)

Gorka Muñoz-Gil, Guillem Guigó i Corominas, Maciej Lewenstein

AI总结:

本研究针对反常扩散实验数据短且含噪、监督学习依赖标注的问题,提出无监督学习方法,可无标注学习扩散特征、区分模型并提取参数,还能发现组合型新扩散类型,适用于监督学习无法应用的实验场景。

摘要:

扩散过程的表征是我们理解各类物理现象的基石。其中许多过程有别于布朗运动,由此产生了反常扩散。如今已有多种理论模型可描述这类过程,但由于现象的随机特性以及获取可靠数据的难度,将这些模型应用于实验装置往往颇具挑战。实验数据通常由短且含噪的轨迹组成,难以用常规统计方法进行表征。近年来,学界通过监督机器学习技术为衔接理论与实验付出了巨大努力,并取得了惊人成果。在本研究中,我们探索了无监督方法在反常扩散数据中的应用。研究表明,无需对数据进行任何标注,即可学习到主要的扩散特征。我们利用该方法区分不同的反常扩散模型,并提取其物理参数。此外,我们还探索了发现新型扩散类型的可行性,这类新型扩散在此处由现有扩散模型的组合形式表示。最后,我们展示了该方法在实验数据中的应用,并证明了其在监督学习无法适用的场景下的优势。

英文摘要:

The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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