引力信息神经网络用于后牛顿双星动力学
Gravity-Informed Neural Networks for Post-Newtonian Binary Dynamics
- Instituto de Física Teórica UAM/CSIC（UAM/CSIC理论物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出引力信息神经网络（GravINNs）框架，将运动方程嵌入训练，用于学习后牛顿双星轨道动力学，涵盖保守与耗散情形，实现单网络替代数值积分，并准确预测轨道演化与能量损失。
中文摘要 AI 辅助
我们引入了一种引力信息神经网络（GravINNs）框架，用于学习后牛顿双星轨道动力学。该方法将运动方程直接纳入训练阶段，并在三个互补层面上展开。首先，我们为保守的后牛顿动力学构建单轨道替代模型，并评估其在不同轨道构型下的准确性。然后，我们将该方法扩展到一个参数模型，该模型跨越由初始间距、径向和切向动量以及对称质量比定义的四维参数空间，使得单个训练网络能够表示整个轨道族，并取代重复的数值积分。最后，我们通过包含$2.5$PN阶的领先辐射反作用贡献来考虑耗散动力学。在此机制下，网络无需使用耗散参考轨迹进行训练，即可准确再现轨道演化和相关的长期能量损失。这些结果表明，物理信息神经网络（PINNs）能够为保守和耗散的后牛顿动力学提供准确且灵活的表示。本工作中使用的完整实现和模型可通过\texttt{GravINNs}仓库公开获取。
英文摘要
We introduce a gravity-informed neural-network (GravINNs) framework for learning post-Newtonian binary orbital dynamics. The method incorporates the equations of motion directly into the training phase and is developed at three complementary levels. First, we construct single-orbit surrogates for conservative post-Newtonian dynamics and assess their accuracy across different orbital configurations. We then extend the approach to a parametric model spanning a four-dimensional parameter space defined by the initial separation, radial and tangential momenta, and symmetric mass ratio, allowing a single trained network to represent entire families of orbits and replace repeated numerical integrations. Finally, we consider dissipative dynamics by including the leading radiation-reaction contribution at $2.5$PN order. In this regime, the network accurately reproduces both the orbital evolution and the associated secular energy loss without using dissipative reference trajectories in the training. These results show that physics-informed neural-networks (PINNs) can provide accurate and flexible representations of both conservative and dissipative post-Newtonian dynamics. The complete implementation and the models used in this work are publicly available through the \texttt{GravINNs} repository.
