Modeling Magnetic Storms' Dynamics with Physics-Informed Neural Networks

Solar activity significantly influences the near-Earth environment, leading to magnetic storms and magnetospheric substorms that can impact both technological and human systems. Understanding the physical processes that govern the Sun-Earth relationship and developing models to forecast magnetic disturbances on Earth are therefore of critical importance. In this context, we present a preliminary work to model and forecast the dynamics of magnetic storms, as measured by the SYM-H geomagnetic index, using Physics-Informed Neural Networks (PINNs). This approach is applied to models based on deterministic ordinary differential equations (ODEs), such as those described by Burton et al. (1975) and others, which were proposed to describe the evolution of geomagnetic indices during magnetic storms. The findings and significance of this approach are discussed in the context of Earth's magnetospheric dynamics and the relevance of PINN techniques in space weather research.