EGU24-10715, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Physics-driven feature combination for an explainable AI approach to flare forecasting

Margherita Lampani1, Sabrina Guastavino1,2, Michele Piana1,2, Federico Benvenuto1, and Anna Maria Massone1
Margherita Lampani et al.
  • 1University of Genova, Department of Mathematics, Genova, Italy
  • 2Istituto Nazionale di Astrofisica (INAF), Osservatorio Astrofisico di Torino, Italy

Typical supervised feature-based machine learning approaches to flare forecasting rely on descriptors extracted from magnetograms, as from Helioseismic and Magnetic Imager (HMI) images, and standardized before being used in the training phase of the machine learning pipeline. However, this artificial intelligence (AI) model does not take into account the physical nature of the features and their role in the plasma physics equations. This talk proposes to generate novel features according to simple physics-driven combinations of the original descriptors, and to show whether this original physically explainable AI model leads to a more predictive solar flare forecasting.

How to cite: Lampani, M., Guastavino, S., Piana, M., Benvenuto, F., and Massone, A. M.: Physics-driven feature combination for an explainable AI approach to flare forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10715,, 2024.

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