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.

翻译：日冕物质抛射（CMEs）对应太阳日冕中大量等离子体与磁场向行星际空间剧烈抛射的现象。CMEs具有重要科学意义，因其参与表征太阳活动期的物理机制。然而近年来，CMEs因对空间天气的影响而备受关注——它们与地磁暴存在关联，并可能诱发太阳高能粒子流的产生。在此空间天气背景下，本文提出一种物理驱动的人工智能（AI）方法用于预测CMEs传播时间，通过利用确定性阻力模型优化级联双神经网络（该网络融合遥感数据与现场数据）的训练过程。研究表明，在AI架构中引入物理信息可显著提升传播时间预测的准确性与鲁棒性。