Graph Neural Networks for Interferometer Simulations
Sidharth Kannan, Pooyan Goodarzi, Evangelos E. Papalexakis, Jonathan W. Richardson
TL;DR
This paper demonstrates that graph neural networks can accurately and efficiently simulate complex optical physics in gravitational-wave interferometers, significantly outperforming traditional simulation methods and providing a new tool for instrumentation design.
Contribution
The paper introduces GNNs for interferometer simulation, achieving high accuracy and 815x faster runtimes, and provides a benchmark dataset for future research.
Findings
GNNs accurately model optical physics in interferometers.
GNNs achieve 815 times faster runtimes than existing methods.
A new high-fidelity simulation dataset is provided for benchmarking.
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.
Peer Reviews
Decision·Submitted to ICLR 2025
The paper is very well-written, and all network design decisions are motivated out of the demands by the physical problem which are introduced in the requisite detailed manner. Especially the description of the physical problem is very well-written and enables an easy understanding of the imposed demands.
The weaknesses of the paper can on a high level be summarized with lack of depth of the evaluation, a lack of embedding into the wider literature, and imprecision in a number of key claims. **Lack of depth of Evaluation** - Specifically table 1 seems to only capture a limited window of the design space. The evaluated models, as well as the dataset evaluations could be improved considerably with a limited amount of effort, such as on the architectural side evaluate "GAT + KAN", "GAT only", "KAN
+ Shows the necessity of using GNN rather than MLP for feature extraction and generalization, as well as the benefit of KAN, as compared to MLP in more actually addressing spatial features such as the prediction of varying spatial intensity distribution. + Results show significant speedups than numerical simulation
Authors should provide more details & clearer explanation about their datasets and the mapping from optics of EM field to a graph. I would highly recommend showing a figure to illustrate an example, including the node features and edge features. About the dataset, can you provide more characteristics? e.g., how many data points, how many nodes, etc. in a table. Also, why the full ALIGO is not covered in the dataset, which seems to be the practical establishment for LIGO. I would expect more
* The paper introduces a new dataset that can be used to predict interferometer simulations. * A GNN model that includes a KAN layer is introduced for the new data. * The model is empirically evaluated and shows some promising initial results.
* Incomplete results: I would expect that the authors compare the results of both the GNN and MLP trained and tested on all possible combinations of datasets. However, Table 1 only contains a subset of these results. In particular, results for the MLP are only provided when trained on the mixed dataset. This makes it impossible to deduce a fair comparison from these results. Also, these partial numbers are only provided for power prediction, for intensity prediction only two numbers are mentione
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Pulsars and Gravitational Waves Research · Model Reduction and Neural Networks