Graph Neural Networks for Interferometer Simulations
This work addresses instrumentation design challenges in physics, providing a new application for GNNs with significant speed improvements, though it is incremental as it extends existing GNN methods to a new domain.
The paper tackled the problem of simulating optical physics for interferometer instrumentation design, specifically applying Graph Neural Networks (GNNs) to LIGO models, achieving runtimes 815 times faster than state-of-the-art simulation packages.
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.