A major breakthrough in solar physics has been achieved by scientists at the University of Graz, in collaboration with researchers from Skoltech. They have utilized artificial intelligence to simulate the magnetic field in the upper atmosphere of the sun in near real-time, leading to exciting possibilities in advancing our understanding of the sun’s behavior and its impact on space weather.
Space weather, driven by the solar magnetic field, can have detrimental effects on critical infrastructures such as electricity, aviation, and space-based technology. Solar active regions, which are areas around sunspots with strong magnetic fields, are the primary source of severe space weather events. Currently, we can only measure the magnetic field at the sun’s surface, while the energy buildup and release occur higher up in the solar atmosphere, known as the corona.
Through the use of physics-informed neural networks, the research team successfully combined observational data with a physical force-free magnetic field model. This integration provided a comprehensive understanding of the relationship between observed phenomena and the underlying physics governing the sun’s activity. This groundbreaking approach represents a significant milestone in solar physics and paves the way for numerical simulations of the sun.
The researchers conducted simulations of a solar active region based on observations and achieved the ability to perform real-time force-free magnetic field simulations. Notably, this process required less than 12 hours of computation time to simulate a five-day observation series. The unprecedented speed of these simulations allows scientists to analyze and forecast solar activity in real-time, significantly enhancing our ability to predict space weather events.
A groundbreaking achievement has been made by a team of scientists from the University of Graz and Skoltech in the realm of solar physics. They utilized artificial intelligence to simulate the magnetic field in the sun’s upper atmosphere, leading to new insights into solar eruptions and their connection to space weather.
Solar eruptions, such as coronal mass ejections characterized by high-speed plasma clouds expelled from the sun, have a direct relationship with the free magnetic energy within the coronal volume. The researchers studied the time evolution of this energy and found strong correlations with observed solar eruptions, confirming the accuracy of their methodology.
The team’s use of artificial intelligence in this context has brought about a significant leap forward in the field. By incorporating observational data into their simulations through AI techniques, they have enhanced their ability to understand and predict solar activity, ultimately improving space weather forecasting.
Robert Jarolim, the lead researcher, emphasized the transformative nature of their approach, stating that AI techniques have the potential to propel simulation capabilities to new heights. Skoltech Associate Professor Tatiana Podlachikova echoed this sentiment, highlighting the promising implications of the increased computing speed for both space weather forecasting and advancing our knowledge of the sun’s behavior.
This research signifies a remarkable breakthrough in solar physics. By leveraging the power of artificial intelligence and physics-informed neural networks, the scientists have achieved real-time simulations of the sun’s coronal magnetic field. This groundbreaking advancement revolutionizes our understanding of solar activity and opens up exciting avenues for further exploration in the field.