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.
@article{arxiv.2512.16051,
title = {Graph Neural Networks for Interferometer Simulations},
author = {Sidharth Kannan and Pooyan Goodarzi and Evangelos E. Papalexakis and Jonathan W. Richardson},
journal= {arXiv preprint arXiv:2512.16051},
year = {2026}
}
Comments
11 pages, 4 figures, Accepted and Presented to the 39th Conference on Neural Information Processing Systems (NeurIPS 2025): AI for Science Workshop