Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation
Abstract This paper constructs the structures of the solar corona (SC) using Fourier neural operators (FNO) based on solar photospheric magnetic field observation. The purpose is to learn the mapping between two infinite‐dimensional function spaces, which takes the photospheric magnetic field as inp...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-05-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024SW003875 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536444813803520 |
|---|---|
| author | Jingmin Zhao Xueshang Feng |
| author_facet | Jingmin Zhao Xueshang Feng |
| author_sort | Jingmin Zhao |
| collection | DOAJ |
| description | Abstract This paper constructs the structures of the solar corona (SC) using Fourier neural operators (FNO) based on solar photospheric magnetic field observation. The purpose is to learn the mapping between two infinite‐dimensional function spaces, which takes the photospheric magnetic field as input and the magnetohydrodynamic (MHD) solar wind plasma parameters as output, from a finite collection of input‐output pairs. The FNO‐SC model is established using MHD simulated results of 36 Carrington rotations (CRs) from 2008, 2009, and 2020. The performance of the FNO‐SC model is tested for 6 CRs during various phases of the solar activity such as descending, minimum, and ascending phases to generate the 3D structures of the SC. With the MHD simulations as references, the average structure similarity index measure (SSIM) value for the magnetic field topology from 1 to 3Rs is around 0.88. From 1 to 20Rs, the SSIM values for the number density and radial speed surpass 0.9. Relative to OMNI observations, the mean absolute percentage error for the radial speed generated from the FNO‐SC model does not exceed 0.25. These results indicate that the FNO‐SC model effectively captures the solar coronal structures typical of the periods investigated, by recovering the MHD simulations as well as the observations. The FNO‐SC model is further trained with enriched data from the maximum phase to assess the capability of modeling such a situation. The FNO‐SC model costs 48.7 s for a single CR prediction, and thus facilitates real‐time space weather forecasting. |
| format | Article |
| id | doaj-art-de8f20a7aebb44e4a050378133599e55 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-de8f20a7aebb44e4a050378133599e552025-01-14T16:27:30ZengWileySpace Weather1542-73902024-05-01225n/an/a10.1029/2024SW003875Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field ObservationJingmin Zhao0Xueshang Feng1SIGMA Weather Group State Key Laboratory for Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaSIGMA Weather Group State Key Laboratory for Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaAbstract This paper constructs the structures of the solar corona (SC) using Fourier neural operators (FNO) based on solar photospheric magnetic field observation. The purpose is to learn the mapping between two infinite‐dimensional function spaces, which takes the photospheric magnetic field as input and the magnetohydrodynamic (MHD) solar wind plasma parameters as output, from a finite collection of input‐output pairs. The FNO‐SC model is established using MHD simulated results of 36 Carrington rotations (CRs) from 2008, 2009, and 2020. The performance of the FNO‐SC model is tested for 6 CRs during various phases of the solar activity such as descending, minimum, and ascending phases to generate the 3D structures of the SC. With the MHD simulations as references, the average structure similarity index measure (SSIM) value for the magnetic field topology from 1 to 3Rs is around 0.88. From 1 to 20Rs, the SSIM values for the number density and radial speed surpass 0.9. Relative to OMNI observations, the mean absolute percentage error for the radial speed generated from the FNO‐SC model does not exceed 0.25. These results indicate that the FNO‐SC model effectively captures the solar coronal structures typical of the periods investigated, by recovering the MHD simulations as well as the observations. The FNO‐SC model is further trained with enriched data from the maximum phase to assess the capability of modeling such a situation. The FNO‐SC model costs 48.7 s for a single CR prediction, and thus facilitates real‐time space weather forecasting.https://doi.org/10.1029/2024SW003875solar coronaFourier neural operatormagnetohydrodynamicsneural networksolar photospheric magnetic field |
| spellingShingle | Jingmin Zhao Xueshang Feng Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation Space Weather solar corona Fourier neural operator magnetohydrodynamics neural network solar photospheric magnetic field |
| title | Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation |
| title_full | Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation |
| title_fullStr | Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation |
| title_full_unstemmed | Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation |
| title_short | Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation |
| title_sort | prediction of solar coronal structures using fourier neural operators based on the solar photospheric magnetic field observation |
| topic | solar corona Fourier neural operator magnetohydrodynamics neural network solar photospheric magnetic field |
| url | https://doi.org/10.1029/2024SW003875 |
| work_keys_str_mv | AT jingminzhao predictionofsolarcoronalstructuresusingfourierneuraloperatorsbasedonthesolarphotosphericmagneticfieldobservation AT xueshangfeng predictionofsolarcoronalstructuresusingfourierneuraloperatorsbasedonthesolarphotosphericmagneticfieldobservation |