标题： dRGT质量引力中接近极值黑洞的准正模使用物理信息神经网络（PINNs） 显示英文标题

标题： Quasi-normal modes of near-extremal black holes in dRGT massive gravity using Physics-Informed Neural Networks (PINNs)

摘要： 在本研究中，我们展示了使用物理信息神经网络（PINNs）计算de Rham-Gabadadze-Tolley（dRGT）大质量引力中的黑洞准正模（QNMs）。这些模式描述了扰动黑洞的振荡频率，在理解这些物体的行为方面非常重要。我们表明，通过仔细选择PINN的超参数，包括网络结构和训练数据，可以在最小模式数的近极限情况下实现计算出的QNMs与近似解析公式之间的良好一致性。我们的结果显示了PINNs在解决QNMs背景下的反问题方面的有效性，并强调了这些算法在提供有关黑洞行为的有价值见解方面的潜力。 显示英文摘要

摘要： In this study, we demonstrate the use of physics-informed neural networks (PINNs) for computing the quasinormal modes (QNMs) of black holes in de Rham-Gabadadze-Tolley (dRGT) massive gravity. These modes describe the oscillation frequencies of perturbed black holes and are important in understanding the behavior of these objects. We show that by carefully selecting the hyperparameters of the PINN, including the network architecture and the training data, it is possible to achieve good agreement between the computed QNMs and the approximate analytical formula in the near-extremal limit for the smallest mode number. Our results demonstrate the effectiveness of PINNs for solving inverse problems in the context of QNMs and highlight the potential of these algorithms for providing valuable insights into the behavior of black holes.