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

10 May 2024

This paper is available on arxiv under CC 4.0 license.


(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).

Abstract and Intro

Data and Methods



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



[3] CAMELS dataset documentation:

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