Graph Neural Networks for Interferometer Simulations
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 18 Dec 2025.
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References · 10
- 2017Graph Attention Networkscited by 8,923
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2020Learning Mesh-Based Simulation with Graph Networkscited by 868
- 2021How Attentive are Graph Attention Networks?cited by 835
- 2024KAN: Kolmogorov-Arnold Networkscited by 649
- 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
- 2024Poseidon: Efficient Foundation Models for PDEscited by 200
- 2024Kolmogorov-Arnold Networks are Radial Basis Function Networkscited by 128
- 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
- 2023Curriculum Learning for ab initio Deep Learned Refractive Opticscited by 32
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