Adaptive Graph Neural Networks for Cosmological Data Generalization: Data and Methods by@cosmological
Adaptive Graph Neural Networks for Cosmological Data Generalization: Data and Methods
by Cosmological thinking: time, space and universal causation May 10th, 2024
Too Long; Didn't Read
Deep learning meets cosmological data with Domain Adaptive Graph Neural Networks for robust parameter extraction.
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).
Table of Links
Acknowledgments and Disclosure of Funding, and References
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.
L O A D I N G
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