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

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...

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Hauptverfasser:	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
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Physics - Instrumentation and Methods for Astrophysics Physics - Solar and Stellar Astrophysics
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