A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3)
Abstract Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing e...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003823 |
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| author | Jiayu Ma Haiyang Fu J. D. Huba Yaqiu Jin |
| author_facet | Jiayu Ma Haiyang Fu J. D. Huba Yaqiu Jin |
| author_sort | Jiayu Ma |
| collection | DOAJ |
| description | Abstract Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI). |
| format | Article |
| id | doaj-art-c91f6b7d8ac142de8e2e168d4b183617 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-c91f6b7d8ac142de8e2e168d4b1836172025-01-14T16:27:28ZengWileySpace Weather1542-73902024-04-01224n/an/a10.1029/2023SW003823A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3)Jiayu Ma0Haiyang Fu1J. D. Huba2Yaqiu Jin3School of Information Science and Engineering Fudan University Shanghai ChinaSchool of Information Science and Engineering Fudan University Shanghai ChinaSyntek Technologies Inc Arlington VA USASchool of Information Science and Engineering Fudan University Shanghai ChinaAbstract Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).https://doi.org/10.1029/2023SW003823ionospheric modelparameter predictionphysics‐informed neural networkSAMI3 |
| spellingShingle | Jiayu Ma Haiyang Fu J. D. Huba Yaqiu Jin A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) Space Weather ionospheric model parameter prediction physics‐informed neural network SAMI3 |
| title | A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) |
| title_full | A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) |
| title_fullStr | A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) |
| title_full_unstemmed | A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) |
| title_short | A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) |
| title_sort | novel ionospheric inversion model pinn sami3 physics informed neural network based on sami3 |
| topic | ionospheric model parameter prediction physics‐informed neural network SAMI3 |
| url | https://doi.org/10.1029/2023SW003823 |
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