  Physics-informed neural networks (PINNs) hold the potential for supplementing
the existing set of techniques for solving differential equations that emerge
in the study of black hole quasinormal modes. The present research investigated
them by studying black hole perturbation equations with known analytical
solutions and thus could be framed as inverse problems in PINNs. Our main goal
was to test the accuracy of PINNs in computing unknown quasinormal frequencies
within the differential equations. The black hole perturbation scenarios that
we considered included near extremal Schwarzschild-de Sitter and
Reissner-Nordstr\"{o}m-de Sitter black holes, and a toy problem resembling
them. For these cases, it was shown that PINNs could compute the QNFs with up
to 4 digit decimal accuracy for the lowest multipole number, $l$, and lowest
mode number, $n$.
