Physics‐Informed Neural Networks for Modeling the Martian Induced Magnetosphere

Abstract Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics‐based, relying on computationally intensive simulations. For the first time, we develop a data‐driven model of the Martian induced magnetospheric magnetic field using Physics‐Informed Neural Network (PINN) combined with MAVEN observations and physical laws. Trained under varying solar wind conditions, the data‐driven model accurately reconstructs the three‐dimensional magnetic field configuration and its variability in response to upstream solar wind drivers. Based on the PINN results, we identify key dependencies of magnetic field configuration on solar wind parameters, including a more negative component in the ‐E hemisphere near the Martian south pole. These findings offer valuable insights into how the configuration of the induced magnetosphere varies with upstream solar wind parameters.