Graph Neural Networks for Interferometer Simulations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917275233157120 |
|---|---|
| author | Kannan, Sidharth Goodarzi, Pooyan Papalexakis, Evangelos E. Richardson, Jonathan W. |
| author_facet | Kannan, Sidharth Goodarzi, Pooyan Papalexakis, Evangelos E. Richardson, Jonathan W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for Interferometer Simulations Kannan, Sidharth Goodarzi, Pooyan Papalexakis, Evangelos E. Richardson, Jonathan W. Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Graph Neural Networks for Interferometer Simulations |
| topic | Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2512.16051 |