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[2512.16051] Graph Neural Networks for Interferometer Simulations
Summary
This article presents a novel application of Graph Neural Networks (GNNs) for simulating interferometer designs, specifically for the LIGO project, demonstrating significant performance improvements over traditional methods.
Why It Matters
The integration of GNNs in astrophysics, particularly in instrumentation design, represents a significant advancement in simulation accuracy and efficiency. This research provides a foundation for future studies and applications in high-energy physics and related fields, potentially transforming how complex optical systems are modeled.
Key Takeaways
- GNNs can simulate complex optical physics in interferometers effectively.
- The proposed method achieves runtimes 815 times faster than current simulation packages.
- A dataset of high-fidelity optical physics simulations is provided for benchmarking.
- This research addresses unique challenges in applying machine learning to physical sciences.
- The findings could influence future instrumentation design in astrophysics.
Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2512.16051 (astro-ph) [Submitted on 18 Dec 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Graph Neural Networks for Interferometer Simulations Authors:Sidharth Kannan, Pooyan Goodarzi, Evangelos E. Papalexakis, Jonathan W. Richardson View a PDF of the paper titled Graph Neural Networks for Interferometer Simulations, by Sidharth Kannan and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG) Cite as: arXiv:2512.16051 [astro-ph.IM] (or arXiv:2512.16051v2 [astro-ph.IM] for...
