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arXiv Machine Learning By Spyros Rigas, Ioannis Contopoulos, Georgios Alexandridis, Antonios Nathanail

An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks

Read the original on arXiv Machine Learning →

arXiv:2606. 10686v1 Announce Type: cross Abstract: The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach and treating the separatrix and equatorial current sheet as infinitesimally thin discontinuities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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