"Gravitational-wave detections by the LIGO-Virgo-KAGRA network have
turned compact-object mergers into precision probes of strong gravity.
The post-merger ringdown is particularly incisive: it is governed by
quasinormal modes (QNMs), the damped oscillations that encode the
remnant's structure and provde a fingerprint of the final object. While
current detectors constrain the dominant mode, next-generation
observatories will resolve multiple modes with high precision, placing
stringent demands on the accuracy of theoretical predictions. Computing
QNMs for rotating black holes is, however, a non-trivial task, as it
requires solving highly coupled, complex-valued perturbation equations
where standard methods struggle. In this talk, I present SpectralPINN, a
hybrid solver combining Pseudo-spectral methods with Physics-Informed
Neural Networks, validated at 10⁻⁵ relative accuracy. I will present
results for Kerr and Kerr-Newman black holes, demonstrating the method's
robustness and accuracy across parameter space, and discuss its
potential for extension to more exotic compact objects relevant to
next-generation detector science."