New Perspectives and Capabilities for Physical Insights into the Magnetosphere in the AI Age

The dynamics of the near-Earth space plasma environment are complex, comprising critical components such as cold plasma, high-energy electrons and protons trapped in the radiation belts, and various electromagnetic waves, including whistler-mode chorus and hiss. These elements pose significant hazards to spacecraft and astronauts. However, the response of these components to external solar wind drivers is highly nonlinear and difficult to predict. Consequently, understanding the underlying physical mechanisms and achieving accurate predictions of their responses have long been significant challenges in space physics and space weather forecasting.

In this presentation, we introduce a novel approach that leverages machine learning (ML) models to study the dynamic distribution and response of the near-Earth space environment. We will discuss the development of several models that accurately reconstruct and predict key parameters, including total electron densities, electron fluxes at various energy levels, and the occurrence and behavior of whistler-mode chorus and hiss waves. These models provide critical insights into how different components of the space environment respond to external drivers, such as solar wind conditions, with important implications for understanding their physical mechanisms.

Moreover, we will demonstrate how these ML models can be integrated with physics-based numerical simulations to investigate the underlying mechanisms responsible for radiation belt dynamics. This hybrid approach, which combines data-driven insights with theoretical frameworks, offers a more comprehensive understanding of space weather processes. Finally, we will showcase the application of interpretable machine learning techniques to extract deeper physical insights, highlighting how these methods can identify the key drivers behind radiation belt dynamics and improve our ability to predict space weather events.