Speaker
Description
Machine learning techniques are increasingly being explored as complementary tools for solving complex problems in gravitational physics. In this talk, I will present recent work on the application of neural-network-based methods to black hole dynamics across different regimes. On the one hand, perturbative models provide an ideal framework for investigating the spectral properties of black hole spacetimes, including quasi-normal modes, scattering phenomena, and the response to generic perturbations. On the other hand, physics-informed neural networks offer a promising approach for solving the nonlinear Einstein equations by directly incorporating the governing equations into the optimization process.
I will discuss recent progress in applying these techniques to problems ranging from black hole perturbation theory to proof-of-concept numerical relativity simulations of binary black hole head-on collisions. Particular emphasis will be placed on the opportunities and challenges of machine learning methods for solving hyperbolic systems of equations relevant to gravitational-wave physics.