日球层物理与空间天气预测中的机器学习：一份发现与建议白皮书
太阳与恒星天体物理
2020-06-23 v1 天体物理仪器与方法
机器学习
摘要
本白皮书的作者于2020年1月16—17日在新泽西理工学院（纽瓦克，新泽西州）相聚，参加为期2天的研讨会，汇集了一组日球层物理学家、数据提供者、专业建模者以及计算机/数据科学家。其目标是讨论在日球层物理中应用机器和/或深度学习技术进行数据分析、建模与预测的关键进展与前景，并为本领域的进一步发展制定战略。研讨会由一系列全体会议（以邀请介绍性报告为特色）与若干开放讨论环节交错组成。讨论成果凝练于本白皮书中，其中还包含了与会者一致同意的顶层建议清单。
引用
@article{arxiv.2006.12224,
title = {Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations},
author = {Gelu Nita and Manolis Georgoulis and Irina Kitiashvili and Viacheslav Sadykov and Enrico Camporeale and Alexander Kosovichev and Haimin Wang and Vincent Oria and Jason Wang and Rafal Angryk and Berkay Aydin and Azim Ahmadzadeh and Xiaoli Bai and Timothy Bastian and Soukaina Filali Boubrahimi and Bin Chen and Alisdair Davey and Sheldon Fereira and Gregory Fleishman and Dale Gary and Andrew Gerrard and Gregory Hellbourg and Katherine Herbert and Jack Ireland and Egor Illarionov and Natsuha Kuroda and Qin Li and Chang Liu and Yuexin Liu and Hyomin Kim and Dustin Kempton and Ruizhe Ma and Petrus Martens and Ryan McGranaghan and Edward Semones and John Stefan and Andrey Stejko and Yaireska Collado-Vega and Meiqi Wang and Yan Xu and Sijie Yu},
journal= {arXiv preprint arXiv:2006.12224},
year = {2020}
}
备注
Workshop Report