Adaptive Graph Neural Networks for Cosmological Data Generalization: Conclusions

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

4 Conclusions

We propose and demonstrate a method for unsupervised DA for cosmological inference with GNNs. We use an MMD-based loss to enable the domain-invariant encoding of features by the GNN. This approach enhances cross-domain robustness: compared to previous methods, DA-GNNs reduce prediction error and improve uncertainty estimates.

Limitations The cross-domain accuracy remains worse when compared to single-domain performance. Although reaching the same accuracy might not be possible, more flexible approaches such as adversarial-based DA techniques [20, 36], instead of distance-based ones such as MMD, might yield better results. Moreover, due to computational and time constraints, our models have been trained and tested only on two of the four available CAMELS simulation suites. Using more suites would yield better cross-domain efficacy and reliability at assessment time. These limitations will be addressed in future work.