The team further studied the time evolution of free magnetic energy within the coronal volume, which is linked to solar eruptive events on the Sun like coronal mass ejections — large plasma clouds ejected from the atmosphere of the Sun at speeds of 100-3,500 km/s. The comparison to extreme ultraviolet observations confirmed the robustness and accuracy of the methodology. Crucially, the results revealed significant depletions of free magnetic energy, both spatially and temporally, which directly correlate with observed solar eruptions.
Robert Jarolim, the lead researcher, commented about the implications of this breakthrough: “Our use of artificial intelligence in this context represents a transformative leap forward. The use of AI techniques for numerical simulations allows us to better incorporate observational data and holds great potential to further advance our simulation capabilities.” Skoltech Associate Professor Tatiana Podlachikova highlights, “The computing speed holds significant promise for improving space weather forecasting and advancing our knowledge of the Sun’s behavior.”
This research conducted by the scientists at the University of Graz and Skoltech represents a remarkable advancement in the field of solar physics. By harnessing the power of AI and physics-informed neural networks, they have achieved real-time simulations of the solar coronal magnetic field, revolutionizing our ability to comprehend solar activity.
The study was developed with the support of Skoltech’s high-performance cluster Zhores for the anticipated Solar Physics Research Integrated Network Group (SPRING) that will provide autonomous monitoring of the Sun using cutting-edge technology of observational solar physics. SPRING is pursued within the SOLARNET project, which is dedicated to the European Solar Telescope initiative supported by the E.U. research and innovation funding program Horizon 2020.