Reconstruction of Magnetohydrodynamic Reconnection Structures by Physics‐Informed Neural Networks (PINNs)

Abstract This study demonstrates the feasibility of applying Physics‐Informed Neural Networks (PINNs) to reconstruct the spatial and temporal evolution of two‐dimensional magnetohydrodynamic (MHD) reconnection structures from limited in situ observational data. By embedding the complete set of MHD equations into the loss function, the reconstructed solutions naturally satisfy the governing physical laws. The reconstruction accuracy is systematically evaluated by varying the number, spatial distribution, and sampling interval of observation points. The analysis reveals that placing observation points both upstream and downstream of the plasmoid significantly enhances reconstruction accuracy, highlighting the importance of capturing both the early‐time evolution near the ‐point and the well‐developed downstream structures. These findings demonstrate the potential of PINNs as a powerful tool for recovering large‐scale MHD reconnection structures from sparse data, while also providing practical guidance for the design and operation of future multi‐satellite observation missions.