Graph Neural Networks for Interferometer Simulations

2025-12-17

AI Summary

Traditional numerical simulations for interferometers such as LIGO are computationally expensive, struggling to balance accuracy and efficiency. To address this, this paper pioneers the application of graph neural networks (GNNs) to precision optical instrument modeling, proposing a physics-guided graph-structured approach: optical paths are explicitly encoded as graphs, with optical parameters serving as node and edge features, and training is supervised by fundamental physical constraints. Key contributions include: (1) introducing the first high-fidelity optical simulation benchmark dataset covering three canonical interferometer topologies; (2) enabling end-to-end prediction of optical responses with accuracy matching state-of-the-art numerical tools (e.g., FINESSE) and achieving an 815× speedup in inference; and (3) demonstrating strong cross-topology generalization and open-sourcing the dataset to advance the emerging “AI for Instrumentation” research direction.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Applying GNNs to simulate LIGO interferometer optical physics
• Achieving faster runtimes than current simulation packages
• Providing a dataset for benchmarking future interferometer simulation work

Innovation

• GNNs simulate LIGO interferometer optical physics
• Achieves 815x faster runtime than existing simulations
• Provides dataset for benchmarking three interferometer topologies