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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-08-01 至 2023-01-31

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中文摘要
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英文摘要
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.
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Collaborative Research: CubeSat Ideas Lab: VIrtual Super-resolution Optics with Reconfigurable Swarms (VISORS)
Travel Support for the Solar Information Processing Workshop; La Roche-en-Ardenne, Belgium; August 18-21, 2014
SHINE: Driving Global Heliospheric Magnetohydrodynamics (MHD) Models with Tomographically-Determined Lower Boundary Conditions
CAREER: Integrated Optical and Radio Ionospheric Remote Sensing and Imaging
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