Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
This provides a more efficient tool for studying solar wind-Mars interactions, advancing atmospheric ion escape research, though it represents an incremental application of PINNs to a new domain.
The researchers tackled the problem of modeling Mars's magnetic field environment by developing the first data-driven model using Physics-Informed Neural Networks (PINNs) combined with MAVEN observations, which accurately reconstructed the three-dimensional magnetic field configuration and its variability under varying solar wind conditions.
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, including B_IMF, P_SW, and θ_cone, 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 the hemispheric asymmetries of the draped field line strength in the Mars-Solar-Electric coordinates. These findings demonstrate the capability of PINNs to reconstruct complex magnetic field structures in the Martian induced magnetosphere, thereby offering a promising tool for advancing studies of solar wind-Mars interactions.