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Machine Learning in Heliophysics and space weather forecasting: a white paper of finding and recommendations

Authors :
Irina Nikolaevna Kitiashvili
Gelu Nita
Manolis Georgoulis
Irina Kitiashvili
Viacheslav Sadykov
Enrico Camporeale
Alexander Kosovichev
Haimin Wang
Vincent Oria
Jason Wang
Rafal Angryk
Berkay Aydin
Azim Ahmadsadeh
Xiaoli Bai
Timothy Bastian
Soukaina Filali Boubrahimi
Bin Chen
Alisdair Davey
Sheldon Fereira
Gregory Fleishman
Dale Gary
Andrew Gerrard
Gregory Hellbourg
Katherine Herbert
Jack Ireland
Egor Illarionov
Natsuha Kuroda
Qin Li
Chang Liu
Yuexin Liu
Hyomin Kim
Dustin Kempton
Ruizhe Ma
Petrus Martens
Ryan Mcgranaghan
Edward Semones
John Stefan
Andrey Stejko
Yaireska Collado Vega
Meiqi Weng
Yang Xu
Sijie Yu
Source :
SAO/NASA Astrophysics Data System.
Publication Year :
2020
Publisher :
United States: NASA Center for Aerospace Information (CASI), 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

Subjects

Subjects :
Aeronautics (General)

Details

Language :
English
Database :
NASA Technical Reports
Journal :
SAO/NASA Astrophysics Data System
Notes :
791926.02.03.06.44.
Publication Type :
Report
Accession number :
edsnas.20205003816
Document Type :
Report