标题： 基于物理信息神经网络的中子星星震学研究 显示英文标题

标题： Towards asteroseismology of neutron stars with physics-informed neural networks

摘要： 研究中子星振荡的引力波特征可能提供有关其内部结构和高密度下状态方程的重要信息。 我们提出了一种基于物理信息神经网络（PINNs）的新技术，用于求解与中子星正常振荡模式相关的特征值问题。 该方法在一种简化场景中进行了测试，该场景具有解析解，可用于测试该方法的性能和准确性。 我们表明，通过该方案可以得到特征频率和特征函数的准确结果。 该方法的灵活性及其适应复杂场景的能力，未来可能作为将更多物理因素纳入这些系统的一种途径。 显示英文摘要

摘要： The study of the gravitational wave signatures of neutron star oscillations may provide important information of their interior structure and Equation of State (EoS) at high densities. We present a novel technique based on physically informed neural networks (PINNs) to solve the eigenvalue problem associated with normal oscillation modes of neutron stars. The procedure is tested in a simplified scenario, with an analytical solution, that can be used to test the performance and the accuracy of the method. We show that it is possible to get accurate results of both the eigenfrequencies and the eigenfunctions with this scheme. The flexibility of the method and its capability of adapting to complex scenarios may serve in the future as a path to include more physics into these systems.