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

TL;DR

This paper presents a physics-driven AI method combining a drag-based model with neural networks to improve the accuracy and robustness of predicting coronal mass ejections' travel times, crucial for space weather forecasting.

Contribution

It introduces a novel AI approach that integrates physical models with neural networks for CME travel time prediction, enhancing performance over purely data-driven methods.

Findings

Physical information improves prediction accuracy.

Enhanced robustness in CME travel time estimates.

Effective integration of remote sensing and in-situ data.

Abstract

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.