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

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