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  • 标题:Space Weather in the Machine Learning Era: A Multidisciplinary Approach
  • 本地全文:下载
  • 作者:E. Camporeale ; S. Wing ; J. Johnson
  • 期刊名称:Space Weather
  • 印刷版ISSN:1542-7390
  • 出版年度:2018
  • 卷号:16
  • 期号:1
  • 页码:2-4
  • DOI:10.1002/2017SW001775
  • 语种:English
  • 出版社:American Geophysical Union
  • 摘要:The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25–29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.
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