Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2020.
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
- 2026Review of Machine Learning Models for Solar Energetic Particle Prediction17 Jun 2026
- 2025Surya: Foundation Model for Heliophysics18 Aug 2025