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arXiv · 2401.01440

Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks

Nirmal Patel·Aycin Aykutalp·Pablo Laguna
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Abstract

Machine learning, particularly neural networks, has rapidly permeated most activities and work where data has a story to tell. Recently, deep learning has started to be used for solving differential equations with input from physics, also known as Physics Informed Neural Networks (PINNs). We present a study showing the efficacy of PINNs for solving the Zerilli and the Regge-Wheeler equations in the time domain to calculate the quasi-normal oscillation modes of a Schwarzschild black hole. We compare the extracted modes with those obtained with finite difference methods. Although the PINN results are competitive, with a few percent differences in the quasi-normal modes estimates relative to those computed with finite difference methods, the real power of PINNs will emerge when applied to large dimensionality problems.

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Nirmal Patel, Aycin Aykutalp, Pablo Laguna. 2024-01-02. Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks. https://arxiv.org/abs/2401.01440

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