mlsanityData to 7 Oct 2026

Conditional Clifford-Steerable CNNs for PDE Modeling

arXiv 2510.14007 ↗First posted 15 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

We introduce Conditional Clifford-Steerable CNNs (C-CSCNNs), a unified framework that incorporates equivariance to arbitrary pseudo-Euclidean groups and significantly improves the expressivity of standard CSCNNs. We show that the kernel basis of the standard formulation is incomplete, limiting model capacity. To address this, we augment the kernels with equivariant representations of the input feature field. We derive the equivariance constraint for these input-dependent kernels and show how it can be solved efficiently via implicit parameterization. We empirically validate on multiple PDE forecasting tasks, including fluid dynamics and relativistic electrodynamics, where our method consistently outperforms standard CSCNNs and performs on par with state-of-the-art baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 60 references that are in the map. First posted on arXiv 15 Oct 2025.

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References · 42

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30 more
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