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Alternative TitleThe application of machine learning in solar physics
刘辉1,2; 季凯帆1; 金振宇1
Source Publication中国科学:物理学 力学 天文学(Scientia Sinica Pysica, Mechanica & Astronomica)
ClassificationP182 ; Tp18
Contribution Rank第1完成单位
Indexed ByCSCD ; 核心
Keyword太阳物理 太阳活动 机器学习 深度学习


Other Abstract

Solar physics has entered the era of big data, and machine learning has gained more and more recognition as a good tool for big data research. This paper reviews the application results of machine learning in solar physics since 2007. Our studies have shown that research in this field has increased significantly during the last four years. Massive solar observation data obtained from various instruments on the ground and in space have been applied, and the topics have covered major aspects of solar physics, such as solar flares, coronal mass ejections, sunspots. Although some good results have emerged and proved that machine learning is suitable for data analysis of solar physics, there has not been a
breakthrough yet. The machines learning methods that used in this field involve classification, regression, clustering, dimensionality reduction, and deep learning. However, classical algorithms, especially classical  lassification method is more popular. This means that the application of machine learning in solar physics is still in its infancy, but it also means
that there is still a lot of work in this field that can be studied in the future.

Funding Project国家自然科学基金[11873027] ; 国家自然科学基金[11773072] ; 国家自然科学基金[11573012] ; 国家自然科学基金[11833010]
Funding Organization国家自然科学基金[11873027, 11773072, 11573012, 11833010]
Subject Area天文学 ; 太阳与太阳系 ; 太阳与太阳系其他学科 ; 计算机科学技术 ; 人工智能 ; 计算机应用
MOST Discipline Catalogue理学 ; 理学::天文学 ; 工学 ; 工学::计算机科学与技术(可授工学、理学学位)
Citation statistics
Document Type期刊论文
Corresponding Author季凯帆
Affiliation1.中国科学院云南天文台, 昆明, 650216
2.昆明理工大学信息工程与自动化学院, 昆明, 650500
First Author AffilicationYunnan Observatories, Chinese Academy of Sciences
Corresponding Author AffilicationYunnan Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
刘辉,季凯帆,金振宇. 机器学习在太阳物理中的应用[J]. 中国科学:物理学 力学 天文学(Scientia Sinica Pysica, Mechanica & Astronomica),2019,49(10):105-117.
APA 刘辉,季凯帆,&金振宇.(2019).机器学习在太阳物理中的应用.中国科学:物理学 力学 天文学(Scientia Sinica Pysica, Mechanica & Astronomica),49(10),105-117.
MLA 刘辉,et al."机器学习在太阳物理中的应用".中国科学:物理学 力学 天文学(Scientia Sinica Pysica, Mechanica & Astronomica) 49.10(2019):105-117.
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