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In this study, we demonstrate the use of physics-informed neural networks(PINNs) for computing the quasinormal modes (QNMs) of black holes in deRham-Gabadadze-Tolley (dRGT) massive gravity. These modes describe theoscillation frequencies of perturbed black holes and are important inunderstanding the behavior of these objects. We show that by carefullyselecting the hyperparameters of the PINN, including the network architectureand the training data, it is possible to achieve good agreement between thecomputed QNMs and the approximate analytical formula in the near-extremal limitfor the smallest mode number. Our results demonstrate the effectiveness ofPINNs for solving inverse problems in the context of QNMs and highlight thepotential of these algorithms for providing valuable insights into the behaviorof black holes.

Quasi-normal modes of near-extremal black holes in dRGT massive gravity using Physics-Informed Neural Networks (PINNs)