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arXiv · 2309.06410

Solving the Pulsar Equation using Physics-Informed Neural Networks

Petros Stefanou·Jorge F. Urbán·José A. Pons
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Abstract

In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied to explore a diverse range of pulsar magneto-spheric models, specifically focusing on axisymmetric cases. The study successfully reproduced various axisymmetric models found in the literature, including those with non-dipolar configurations, while effectively characterizing current sheet features. Energy losses in all studied models were found to exhibit reasonable similarity, differing by no more than a factor of three from the classical dipole case. This research lays the groundwork for a reliable elliptic Partial Differential Equation solver tailored for astrophysical problems. Based on these findings, we foresee that the utilization of PINNs will become the most efficient approach in modelling three-dimensional magnetospheres. This methodology shows significant potential and facilitates an effortless generalization, contributing to the advancement of our understanding of pulsar magnetospheres.

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Petros Stefanou, Jorge F. Urbán, José A. Pons. 2023-09-12. Solving the Pulsar Equation using Physics-Informed Neural Networks. https://doi.org/10.1093/mnras%2Fstad2840

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