Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
It provides guidance for researchers and practitioners in heliophysics to improve data analysis and forecasting, but it is incremental as it consolidates existing discussions rather than presenting new results.
This white paper addresses the application of machine and deep learning techniques in heliophysics and space weather forecasting by summarizing findings and recommendations from a workshop, aiming to shape future developments in the field.
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.