Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915684365107200 |
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| author | Gao, Jiawei Dong, Chuanfei Zhang, Chi Qin, Yilan Shekarpaz, Simin Li, Xinmin Wang, Liang Zhou, Hongyang Tadlock, Abigail |
| author_facet | Gao, Jiawei Dong, Chuanfei Zhang, Chi Qin, Yilan Shekarpaz, Simin Li, Xinmin Wang, Liang Zhou, Hongyang Tadlock, Abigail |
| contents | Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics-based, relying on computationally intensive simulations. For the first time, we develop a data-driven model of the Martian induced magnetospheric magnetic field using Physics-Informed Neural Network (PINN) combined with MAVEN observations and physical laws. Trained under varying solar wind conditions, including B_IMF, P_SW, and θ_cone, the data-driven model accurately reconstructs the three-dimensional magnetic field configuration and its variability in response to upstream solar wind drivers. Based on the PINN results, we identify key dependencies of magnetic field configuration on solar wind parameters, including the hemispheric asymmetries of the draped field line strength in the Mars-Solar-Electric coordinates. These findings demonstrate the capability of PINNs to reconstruct complex magnetic field structures in the Martian induced magnetosphere, thereby offering a promising tool for advancing studies of solar wind-Mars interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere Gao, Jiawei Dong, Chuanfei Zhang, Chi Qin, Yilan Shekarpaz, Simin Li, Xinmin Wang, Liang Zhou, Hongyang Tadlock, Abigail Earth and Planetary Astrophysics Machine Learning Space Physics Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics-based, relying on computationally intensive simulations. For the first time, we develop a data-driven model of the Martian induced magnetospheric magnetic field using Physics-Informed Neural Network (PINN) combined with MAVEN observations and physical laws. Trained under varying solar wind conditions, including B_IMF, P_SW, and θ_cone, the data-driven model accurately reconstructs the three-dimensional magnetic field configuration and its variability in response to upstream solar wind drivers. Based on the PINN results, we identify key dependencies of magnetic field configuration on solar wind parameters, including the hemispheric asymmetries of the draped field line strength in the Mars-Solar-Electric coordinates. These findings demonstrate the capability of PINNs to reconstruct complex magnetic field structures in the Martian induced magnetosphere, thereby offering a promising tool for advancing studies of solar wind-Mars interactions. |
| title | Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere |
| topic | Earth and Planetary Astrophysics Machine Learning Space Physics |
| url | https://arxiv.org/abs/2512.16175 |