Towards asteroseismology of neutron stars with physics-informed neural networks
arXiv:2504.12183 · doi:10.1140/epjc/s10052-025-14942-z
Abstract
The study of the gravitational wave signatures of neutron star oscillations may provide important information of their interior structure and Equation of State (EoS) at high densities. We present a novel technique based on physically informed neural networks (PINNs) to solve the eigenvalue problem associated with normal oscillation modes of neutron stars. The procedure is tested in a simplified scenario, with an analytical solution, that can be used to test the performance and the accuracy of the method. We show that it is possible to get accurate results of both the eigenfrequencies and the eigenfunctions with this scheme. The flexibility of the method and its capability of adapting to complex scenarios may serve in the future as a path to include more physics into these systems.
arXiv comment21 pages, 7 figures, submitted to IOP Publishing: Machine Learning: Science and Technology
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