cs.LGMar 9, 2026

Integral Formulas for Vector Signal Tensor Products

Authors: Valentin Heyraud, Zachary Weller-Davies, Jules Tilly

Organizations: InstaDeep, 1 Triton Square, London, NW1 3BF, United Kingdom

Abstract

We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor product to anti-symmetric couplings. In particular, we obtain explicit closed-form expressions for the anti-symmetric analogues of the Gaunt coefficients. This enables us to simulate the Clebsch-Gordan tensor product using a single Vector Signal Tensor Product, yielding up to a 9×9\times reduction in the required tensor product evaluations. Our results enable efficient and practical implementations of the Vector Signal Tensor Product, paving the way for applications of this generalization of Gaunt Tensor Products in SO(3)\mathrm{SO}(3)-equivariant neural networks. Moreover, we discuss how the Gaunt and the Vector Signal Tensor Products allow to control the expressivity-runtime tradeoff associated with the usual Clebsch-Gordan Tensor Products. Finally, we investigate low rank decompositions of the normalizations of the considered tensor products in view of their use in equivariant neural networks.

Explore similar work

CardsList
  1. Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics

    Feb 25, 2026YuQing Xie, Ameya Daigavane, Mit Kotak +1Spherical HarmonicsEquivariant Neural Networks

  2. Exact Symmetry as Algebra: Group-Structured Tensors that Enforce Physical Selection Rules

    May 19, 2026Paulina Hoyos, Shashanka Ubaru, Dongsung Huh +5Scientific DiscoveryEquivariant Neural Networks

  3. Spin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)\mathrm{E}(3)-Equivariant Networks

    Jul 1, 2026Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5Machine Learning Interatomic PotentialsEquivariant Neural Networks