In recent years, graph neural networks (GNNs) have shown tremendous promise in solving problems in high energy physics, materials science, and fluid dynamics. In this work, we introduce a new application for GNNs in the physical sciences: instrumentation design. As a case study, we apply GNNs to simulate models of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and show that they are capable of accurately capturing the complex optical physics at play, while achieving runtimes 815 times faster than state of the art simulation packages. We discuss the unique challenges this problem provides for machine learning models. In addition, we provide a dataset of high-fidelity optical physics simulations for three interferometer topologies, which can be used as a benchmarking suite for future work in this direction.

翻译：近年来，图神经网络（GNNs）在解决高能物理、材料科学和流体动力学领域的问题中展现出巨大潜力。本研究将图神经网络引入物理科学中的一个新应用领域：仪器设计。作为案例研究，我们将图神经网络应用于激光干涉引力波天文台（LIGO）模型的模拟，并证明其能够精确捕捉复杂的光学物理过程，同时实现比当前最先进模拟软件包快815倍的运行速度。我们讨论了该问题对机器学习模型提出的独特挑战。此外，我们提供了三种干涉仪拓扑结构的高保真光学物理模拟数据集，该数据集可作为该方向未来研究工作的基准测试套件。