Physics-driven machine learning for the prediction of coronal mass ejections' travel times
This work addresses space weather forecasting for stakeholders like satellite operators and power grids, but it is incremental as it builds on existing models with AI integration.
The paper tackled predicting coronal mass ejections' travel times by introducing a physics-driven AI approach that uses a deterministic drag-based model to enhance neural network training, resulting in significantly improved accuracy and robustness.
Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are scientifically relevant because they are involved in the physical mechanisms characterizing the active Sun. However, more recently CMEs have attracted attention for their impact on space weather, as they are correlated to geomagnetic storms and may induce the generation of Solar Energetic Particles streams. In this space weather framework, the present paper introduces a physics-driven artificial intelligence (AI) approach to the prediction of CMEs travel time, in which the deterministic drag-based model is exploited to improve the training phase of a cascade of two neural networks fed with both remote sensing and in-situ data. This study shows that the use of physical information in the AI architecture significantly improves both the accuracy and the robustness of the travel time prediction.