Machine Learning in Space Weather Prediction

Dear Colleagues,

This Special Issue is dedicated to exploring the transformative role of machine learning (ML) in advancing space weather prediction. Space weather events—including solar flares, coronal mass ejections, and geomagnetic storms—pose significant risks to satellite operations, power grids, and astronaut safety. While traditional physics-based models often face challenges in capturing the nonlinear and complex dynamics of space plasmas, ML techniques such as deep learning, transfer learning, and uncertainty quantification offer powerful tools for analyzing multi-source space data and improving the forecasting of extreme events. We invite original research, methodological advances, and review articles that investigate ML applications across solar–terrestrial physics, radiation belt dynamics, and space weather risk mitigation.

To potential contributors: Your interdisciplinary research bridging ML, space physics, and engineering is crucial for driving progress in this high-stakes field—we warmly welcome your innovative submissions.

To readers: This issue will provide a curated collection of cutting-edge ML approaches to space weather prediction, serving as a key reference for researchers, industry professionals, and policymakers committed to enhancing space weather resilience.

Prof. Dr. Gang Qin
Dr. Yang Wang
Guest Editors

Manuscript Submission Information

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