Space Weather in the Machine Learning Era: A Multidisciplinary Approach-Reference-Cited by-同舟云学术

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Space Weather in the Machine Learning Era: A Multidisciplinary Approach

Published:2018-01 Issue:1 Volume:16 Page:2-4
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Camporeale E.¹^ORCID,Wing S.²^ORCID,Johnson J.³,Jackman C. M.⁴,McGranaghan R.⁵⁶^ORCID

Affiliation:

1. Centrum Wiskunde & Informatica; Amsterdam Netherlands

2. Applied Physics Laboratory; The Johns Hopkins University; Laurel MA USA

3. Department of Engineering and Computer Science; Andrews University; Berrien Springs MI USA

4. Department of Physics and Astronomy; University of Southampton; Southampton UK

5. University Corporation for Atmospheric Research; Boulder CO USA

6. NASA Jet Propulsion Laboratory; California Institute of Technology; Pasadena CA USA

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/2017SW001775/fullpdf

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1. Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches;The Astrophysical Journal;2024-08-01

2. A machine learning approach for estimating the drift velocities of equatorial plasma bubbles based on All-Sky Imager and GNSS observations;Advances in Space Research;2024-08

3. SPASE metadata as a building block of a heliophysics science-enabling framework;Advances in Space Research;2023-12

4. Which Upstream Solar Wind Conditions Matter Most in Predicting B_z Within Coronal Mass Ejections;Space Weather;2023-03-29

5. Redefining flux ropes in heliophysics;Frontiers in Astronomy and Space Sciences;2023-03-08

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