The First Conference on Machine Learning in Heliophysics; Amsterdam, Netherlands; September 16-20, 2019

第一届太阳物理学机器学习会议；

• 批准号: 1938772
• 负责人: Farzad Kamalabadi
• 金额: $ 4.14万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938772&HistoricalAwards=false
• 关键词: First; Conference; Machine; Learning; Heliophysics

This 1-year project is to enable the participation of graduate students and early-career researchers at the first conference on Machine Learning in Heliophysics, which will be held in Amsterdam, Netherlands, during September 16-20, 2019. This international conference is entirely focused on the use of machine learning and information processing tools and techniques in heliophysics, thus encompassing the fields of solar physics, Sun-Earth relationships, magnetospheric and ionospheric physics. The goal of this first-of-its-kind conference is to improve present understanding of heliophysics phenomena and their dynamics, as well as our ability to predict space weather events by leveraging advancements happening at a fast pace in the machine learning and related communities. The practical objective of the conference is two-fold: (i) inform the international community on recent advancements in the use of machine learning and related tools and techniques in heliophysics; and, (ii) connect the physics and the computer science communities on the overlapping and multi-disciplinary topic of machine learning. The research and EPO agenda of this 1-year project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个为期一年的项目是为了让研究生和早期职业研究人员参加将于2019年9月16日至20日在荷兰阿姆斯特丹举行的第一届太阳物理学机器学习会议。本次国际会议完全聚焦于在太阳物理学中使用机器学习和信息处理工具和技术，从而涵盖了太阳物理、日地关系、磁层和电离层物理等领域。这一首次会议的目标是通过利用机器学习和相关社区的快速发展，提高目前对太阳物理现象及其动力学的理解，以及我们预测空间天气事件的能力。会议的实际目标有两个：(i)向国际社会通报在太阳物理学中使用机器学习和相关工具和技术的最新进展；并且，（ii）在机器学习的重叠和多学科主题上连接物理学和计算机科学界。这个为期一年的项目的研究和EPO议程支持AGS部门在发现、学习、多样性和跨学科研究方面的战略目标。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。