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Description
The magnetospheres of compact objects are known to be at the origin of many important astrophysical phenomena, going from pulsar emission to jet formation around black holes through the Blandford-Znajek process. Thanks to variety of numerical tools, these phenomena are increasingly well understood. However in the era of multi-messenger astrophysics comes the challenge of understanding the nature of new transients such as the merger of binary objects, where the interaction of magnetospheres might play an important role. While these have been the subject of recent studies, their parameter space is very big, and still quite unexplored. To tackle these challenges, new numerical schemes have been proposed. In this paper, we use Physics Informed Neural Networks (PINNs) to evolve the magnetospheres of compact objects within the framework of force-free magnetohydrodynamics. We find that these tools can reproduce the expected behaviors for a range of standard situations.