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Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma andmagnetic field from the solar corona into the heliosphere. CMEs arescientifically relevant because they are involved in the physical mechanismscharacterizing the active Sun. However, more recently CMEs have attractedattention for their impact on space weather, as they are correlated togeomagnetic storms and may induce the generation of Solar Energetic Particlesstreams. In this space weather framework, the present paper introduces aphysics-driven artificial intelligence (AI) approach to the prediction of CMEstravel time, in which the deterministic drag-based model is exploited toimprove the training phase of a cascade of two neural networks fed with bothremote sensing and in-situ data. This study shows that the use of physicalinformation in the AI architecture significantly improves both the accuracy andthe robustness of the travel time prediction.

Physics-driven machine learning for the prediction of coronal mass ejections' travel times