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

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Title: Reconstruction of Magnetohydrodynamic Reconnection Structures by Physics‐Informed Neural Networks (PINNs).
Authors: Isayama, S.1,2,3 (AUTHOR) isayama@esst.kyushu-u.ac.jp, Shimooka, H.4 (AUTHOR), Kono, R.4 (AUTHOR), Matsukiyo, S.1,2,3,5 (AUTHOR)
Source: Journal of Geophysical Research. Space Physics. Feb2026, Vol. 131 Issue 2, p1-13. 13p.
Subject Terms: Magnetic reconnection, Magnetohydrodynamics, Computational physics, Satellite-based remote sensing, Plasma physics
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 X $X$‐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. Plain Language Summary: Magnetic reconnection rapidly converts magnetic energy into particle energy and drives phenomena such as auroras and solar flares. Because satellites observe plasma conditions only along their flight paths, it is difficult to grasp the full spatial and temporal structure of reconnection from limited observations. In this study, we test a physics‐informed neural network (PINN) approach using benchmark data generated by two‐dimensional MHD simulations. PINNs incorporate the governing physical laws into the training process, allowing them to recover the evolution of reconnection structures from limited virtual observational data. The results show that reconstruction accuracy depends strongly on where observation points are placed. Using points both upstream and downstream of the reconnection site greatly improves performance, because this configuration captures both early changes near the X‐point and later downstream development. These findings highlight the potential of PINNs for reconstructing large‐scale plasma structures and guiding future multi‐satellite missions. Key Points: Physics‐Informed Neural Networks (PINNs) reconstruct 2D spatiotemporal magnetohydrodynamic reconnection structures from limited in situ observationsPlacing satellites upstream and downstream of the reconnection site greatly improves reconstruction accuracyPINN‐based reconstructions recover small‐scale features finer than the satellite spacing [ABSTRACT FROM AUTHOR]
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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 X $X$‐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. Plain Language Summary: Magnetic reconnection rapidly converts magnetic energy into particle energy and drives phenomena such as auroras and solar flares. Because satellites observe plasma conditions only along their flight paths, it is difficult to grasp the full spatial and temporal structure of reconnection from limited observations. In this study, we test a physics‐informed neural network (PINN) approach using benchmark data generated by two‐dimensional MHD simulations. PINNs incorporate the governing physical laws into the training process, allowing them to recover the evolution of reconnection structures from limited virtual observational data. The results show that reconstruction accuracy depends strongly on where observation points are placed. Using points both upstream and downstream of the reconnection site greatly improves performance, because this configuration captures both early changes near the X‐point and later downstream development. These findings highlight the potential of PINNs for reconstructing large‐scale plasma structures and guiding future multi‐satellite missions. Key Points: Physics‐Informed Neural Networks (PINNs) reconstruct 2D spatiotemporal magnetohydrodynamic reconnection structures from limited in situ observationsPlacing satellites upstream and downstream of the reconnection site greatly improves reconstruction accuracyPINN‐based reconstructions recover small‐scale features finer than the satellite spacing [ABSTRACT FROM AUTHOR]
ISSN:21699380
DOI:10.1029/2025JA034515