We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physics-informed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and non-eccentric orbits. We show the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a new, data-driven approach to studying the dynamics of binary black hole systems.

翻译：我们引入了引力波形反向战略, 发现二进制黑洞( BBH) 系统的机械模型。 我们显示, 只需用一个单一的时间序列( 可能噪音) 的波形数据来构建 BBH 系统的运动方程式。 我们的战略首先从一组通用差异方程式开始, 由Feed- forforward神经网络参数化, 我们的战略就是在这个空间内建造一个合理的机械模型空间, 并在物理上进行有限的优化, 以尽量减少波形错误 。 我们对各种 BBH 系统采用我们的方法, 包括偏心和非偏心轨道中的极端和可比的质量比重系统。 我们显示, 由此产生的差方程式适用于比训练间隔长的时间长度, 并且自动计算出相拉效应, 如 围状偏、 辐射反应 和 轨道跳跃等 。 这里概述的方法为研究二进黑洞系统的动态提供了一种由数据驱动的新方法 。