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September 10, 2025The Astrophysical JournalOpen Access

Cosmology with Topological Deep Learning

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Topological neural networks improve mean squared error in cosmological models, suggesting enhanced structure representation.

Topological neural networks effectively capture hierarchical structures in cosmology and improve modeling accuracy.
Compared to traditional graph neural networks, new models reduce mean squared error significantly, achieving up to 60% improvement in σ 8.
The combination of higher-order structures and message-passing leads to enhanced expressive power in neural networks.
Ensuring E (3) invariance highlights the network's robustness against geometric transformations.

Lee et al. (2025) studied this question.

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