Physics-driven machine learning for the prediction of coronal mass
  ejections' travel times
Sabrina Guastavino Valentina Candiani Alessandro Bemporad Francesco Marchetti Federico Benvenuto Anna Maria Massone Roberto Susino Daniele Telloni Silvano Fineschi Michele Piana
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.