About this Research Topic
In recent years, the explosion in computing power and the amount of accessible data has resulted in a subsequent growth in applications of machine learning and statistical methods across many disciplines. The use of these methods in astronomy and space sciences has seen advances across the scientific process, from modelling to data analysis. Various machine learning and statistical methods are now being adopted in solar flare forecasting, enabling early warning of strong solar flare events.
The goal of this topic is to summarize and compare cutting edge machine learning and statistical methods for data driven solar flare forecasting. Some topics of interest for this Research Topic may include, but are not limited to :
1. Solar flare prediction with quantified uncertainty
2. Operational solar flare forecasting
3. Interpretable solar flare forecasting
4. Solar flare related events forecasting, e.g. CME and SEP
5. Physics-informed solar flare forecasting
6. Data driven methods in solar flare forecasting that informs new physics
This research topic welcomes both original work and reviews.
The goal of this topic is to summarize and compare cutting edge machine learning and statistical methods for data driven solar flare forecasting. Some topics of interest for this Research Topic may include, but are not limited to :
1. Solar flare prediction with quantified uncertainty
2. Operational solar flare forecasting
3. Interpretable solar flare forecasting
4. Solar flare related events forecasting, e.g. CME and SEP
5. Physics-informed solar flare forecasting
6. Data driven methods in solar flare forecasting that informs new physics
This research topic welcomes both original work and reviews.
Keywords: Machine Learning, solar flare prediction, CME, SEP
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
