Speaker
Description
The detection of gravitational waves has turned the study of quasinormal modes (QNMs) into a fundamental tool for testing General Relativity. However, traditional numerical methods often face stability challenges when dealing with massive fields or high-order overtones. In this work, we present a robust computational framework based on Physics-Informed Neural Networks (PINNs) to solve the eigenvalue problem associated with the Regge-Wheeler equation. Our approach incorporates a tailored ansatz to enforce radiation boundary conditions and utilizes a hybrid optimization strategy, combining the Adam algorithm for global exploration with the L-BFGS method for high-precision refinement. We demonstrate that this architecture achieves unprecedented residual accuracy, in the order of $10^{-7}$, effectively capturing the complex frequency spectrum for both massless and massive scalar fields ($\mu = 0.1$). Our results show excellent agreement with traditional spectral methods and highlight the PINN’s stability in resolving overtones up to $n=3$. This framework establishes a flexible and efficient path for exploring modified gravity theories in future research.