Adaptive Graph Neural Networks for Cosmological Data Generalization: Data and Methods

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This paper is available on arxiv under CC 4.0 license.

Authors:

(1) Andrea Roncoli, Department of Computer, Science (University of Pisa);

(2) Aleksandra Ciprijanovi“c“, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago);

(3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory);

(4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute);

(5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago).

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Table of Links

Abstract and Intro

Data and Methods

Results

Conclusions

Acknowledgments and Disclosure of Funding, and References

Additional Plots

2 Data and Methods

2.1 Domain Adaptation


Optimization and Computing Resources We performed experiments on NVIDIA A100 40GB GPU. For each of the models, implemented using PyTorch Geometric [19], we perform a hyperparameter search using the Optuna library [1], with 50 trials per model. More details on code performance, model implementations, and selected hyperparameters can be found in the publicly available code[4].

2.2 Evaluation

[1] https://arepo-code.org/


[2] http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.html


[3] CAMELS dataset documentation: https://camels.readthedocs.io/en/latest/index.html


[4] GitHub repository will be added after the anonymous review stage.

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