cover image: Modelling solar coronal magnetic fields with physics-informed neural networks

20.500.12592/5shvhoa

Modelling solar coronal magnetic fields with physics-informed neural networks

1 Jan 2024

We present a novel numerical approach aiming at computing equilibria and dynamics structures of magnetized plasmas in coronal environments. A technique based on the use of neural networks that integrates the partial differential equations of the model, and called physics-informed neural networks (PINNs), is introduced. The functionality of PINNs is explored via calculation of different magnetohydrodynamic (MHD) equilibrium configurations, and also obtention of exact two-dimensional steady-state magnetic reconnection solutions. Advantages and drawbacks of PINNs compared to traditional numerical codes are discussed in order to propose future improvements. Interestingly, PINNs is a meshfree method in which the obtained solution and associated different order derivatives are quasi-instantaneously generated at any point of the spatial domain. We believe that our results can help to pave the way for future developments of time dependent MHD codes based on PINNs.

Authors

H. Baty, V. Vigon

Bibliographic Reference
H. Baty, V. Vigon. Modelling solar coronal magnetic fields with physics-informed neural networks. Monthly Notices of the Royal Astronomical Society, 2024, 527, pp.2575-2584. ⟨10.1093/mnras/stad3320⟩. ⟨insu-04295010⟩
DOI
https://doi.org/10.1093/mnras/stad3320
HAL Collection
["INSU - Institut National des Sciences de l'Univers", 'CNRS - Centre national de la recherche scientifique', 'Université de Strasbourg', 'Archive ouverte du site Alsace']
HAL Identifier
4295121
Institution
['Université de Strasbourg', "Institut national des sciences de l'Univers"]
Laboratory
Observatoire astronomique de Strasbourg
Published in
France

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