用图神经网络加速引力波仪光学仿真，快815倍。
Graph Neural Networks for Interferometer Simulations
- 用图神经网络建模干涉仪光学系统，捕捉复杂物理过程。
- 仿真速度比现有工具快815倍，精度保持高水平。
- 适合从事物理仿真与机器学习交叉研究的团队使用。
近年来，图神经网络（GNN）在高能物理、材料科学和流体动力学等领域展现出巨大潜力。本文将GNN引入仪器设计领域，以激光干涉引力波天文台（LIGO）为案例，证明其能准确模拟复杂的光学物理过程，同时相比最先进的仿真软件，运行速度提升815倍。文中还探讨了该问题对机器学习模型的独特挑战，并提供了一个包含三种干涉仪拓扑结构的高保真光学仿真数据集，可用于未来研究的基准测试。
原文摘要 · Abstract (English)
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.
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