Reconstruction of Magnetohydrodynamic Reconnection Structures by Physics‐Informed Neural Networks (PINNs).
Saved in:
| 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] |
| Copyright of Journal of Geophysical Research. Space Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: 8gh DbLabel: GreenFILE An: 191834875 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Reconstruction of Magnetohydrodynamic Reconnection Structures by Physics‐Informed Neural Networks (PINNs). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Isayama%2C+S%2E%22">Isayama, S.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> isayama@esst.kyushu-u.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Shimooka%2C+H%2E%22">Shimooka, H.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kono%2C+R%2E%22">Kono, R.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Matsukiyo%2C+S%2E%22">Matsukiyo, S.</searchLink><relatesTo>1,2,3,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Space+Physics%22">Journal of Geophysical Research. Space Physics</searchLink>. Feb2026, Vol. 131 Issue 2, p1-13. 13p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+reconnection%22">Magnetic reconnection</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetohydrodynamics%22">Magnetohydrodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+physics%22">Computational physics</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Plasma+physics%22">Plasma physics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Space Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=191834875 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2025JA034515 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Magnetic reconnection Type: general – SubjectFull: Magnetohydrodynamics Type: general – SubjectFull: Computational physics Type: general – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: Plasma physics Type: general Titles: – TitleFull: Reconstruction of Magnetohydrodynamic Reconnection Structures by Physics‐Informed Neural Networks (PINNs). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Isayama, S. – PersonEntity: Name: NameFull: Shimooka, H. – PersonEntity: Name: NameFull: Kono, R. – PersonEntity: Name: NameFull: Matsukiyo, S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21699380 Numbering: – Type: volume Value: 131 – Type: issue Value: 2 Titles: – TitleFull: Journal of Geophysical Research. Space Physics Type: main |
| ResultId | 1 |
