Quasinormal Modes in Modified Gravity using Physics-Informed Neural Networks
arxiv(2024)
摘要
In this paper, we apply a novel approach based on physics-informed neural
networks to the computation of quasinormal modes of black hole solutions in
modified gravity. In particular, we focus on the case of
Einstein-scalar-Gauss-Bonnet theory, with several choices of the coupling
function between the scalar field and the Gauss-Bonnet invariant. This type of
calculation introduces a number of challenges with respect to the case of
General Relativity, mainly due to the extra complexity of the perturbation
equations and to the fact that the background solution is known only
numerically. The solution of these perturbation equations typically requires
sophisticated numerical techniques that are not easy to develop in
computational codes. We show that physics-informed neural networks have an
accuracy which is comparable to traditional numerical methods in the case of
numerical backgrounds, while being very simple to implement. Additionally, the
use of GPU parallelization is straightforward thanks to the use of standard
machine learning environments.
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