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

arXiv: https://arxiv.org/abs/2305.10057 · HTML: https://mlsanity.com/paper/2305.10057/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 2 of 2)

- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Operational solar flare forecasting via video-based deep learning](https://mlsanity.com/paper/2209.05128/) (2022)

## Built on by (most cited first, 6 of 6)

- [Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models](https://mlsanity.com/paper/2312.01691/) (2023)
- [Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches](https://mlsanity.com/paper/2403.09847/) (2024)
- [Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model](https://mlsanity.com/paper/2503.03237/) (2025)
- [Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning](https://mlsanity.com/paper/2501.01011/) (2025)
- [Greedy feature selection: Classifier-dependent feature selection via greedy methods](https://mlsanity.com/paper/2403.05138/) (2024)
- [Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations](https://mlsanity.com/paper/2605.24748/) (2026)

## Abstract (arXiv)

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.