Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
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太阳物理学和空间天气预报中的机器学习：调查结果和建议白皮书
DOI：
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sijie Yu
中科院分区：
文献类型：
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作者：
G. Nita;M. Georgoulis;I. Kitiashvili;V. Sadykov;E. Camporeale;A. Kosovichev;Haimin Wang;Vincent Oria;J. Wang;R. Angryk;Berkay Aydin;Azim Ahmadzadeh;X. Bai;T. Bastian;S. F. Boubrahimi;Bin Chen;A. Davey;Sheldon Fereira;G. Fleishman;D. Gary;A. Gerrard;G. Hellbourg;K. Herbert;J. Ireland;E. Illarionov;Natsuha Kuroda;Qin Li;Chang Liu;Yuexin Liu;Hyomin Kim;Dustin J. Kempton;Ruizhe Ma;P. Martens;R. McGranaghan;E. Semones;J. Stefan;A. Stejko;Y. Collado;Meiqi Wang;Yan Xu;Sijie Yu
这篇白色论文的作者于2020年1月16日至17日在新泽西州纽瓦克的新泽西理工学院举行了为期两天的研讨会，汇集了一组太阳能电池组，数据提供者，专家建模者和计算机/数据科学家。他们的目标是讨论机器和/或深度学习技术在太阳物理学数据分析、建模和预测中应用的关键发展和前景，并为该领域的进一步发展制定战略。研讨会结合了一系列全体会议，邀请介绍性演讲与一系列公开讨论会交织在一起。讨论的结果概括在这份白色文件中，其中还列出了与会者商定的最高级别建议清单。
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.
RADYNVERSION：学习使用可逆神经网络反转太阳耀斑大气
DOI：
10.3847/1538-4357/ab07b4
发表时间：
2019
期刊：
The Astrophysical Journal
影响因子：
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作者：
Osborne C
通讯作者：
Osborne C