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Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

Authors :
Nita, Gelu
Georgoulis, Manolis
Kitiashvili, Irina
Sadykov, Viacheslav
Camporeale, Enrico
Kosovichev, Alexander
Wang, Haimin
Oria, Vincent
Wang, Jason
Angryk, Rafal
Aydin, Berkay
Ahmadzadeh, Azim
Bai, Xiaoli
Bastian, Timothy
Boubrahimi, Soukaina Filali
Chen, Bin
Davey, Alisdair
Fereira, Sheldon
Fleishman, Gregory
Gary, Dale
Gerrard, Andrew
Hellbourg, Gregory
Herbert, Katherine
Ireland, Jack
Illarionov, Egor
Kuroda, Natsuha
Li, Qin
Liu, Chang
Liu, Yuexin
Kim, Hyomin
Kempton, Dustin
Ma, Ruizhe
Martens, Petrus
McGranaghan, Ryan
Semones, Edward
Stefan, John
Stejko, Andrey
Collado-Vega, Yaireska
Wang, Meiqi
Xu, Yan
Yu, Sijie
Publication Year :
2020

Abstract

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.<br />Comment: Workshop Report

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2006.12224
Document Type :
Working Paper