计算同质空间上的等变矩阵,用于几何深度学习和非定态李代数

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-04-11 DOI:10.1007/s10444-024-10126-7
Vincent Knibbeler
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引用次数: 0

摘要

我们开发了一种基本方法,用于计算从李群 G 的均相空间 G/H 到该群模块的等变映射空间。该李群不要求紧凑。更一般地说,我们研究均相向量束中的不变截面空间,并对纤维是代数的情况特别感兴趣。后一种情况具有天然的全局代数结构。我们对同质空间具有紧凑稳定器的情况下的这些自形代数进行了分类。这项工作可应用于几何深度学习的理论发展,也可应用于无定形李代数理论。
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Computing equivariant matrices on homogeneous spaces for geometric deep learning and automorphic Lie algebras

We develop an elementary method to compute spaces of equivariant maps from a homogeneous space G/H of a Lie group G to a module of this group. The Lie group is not required to be compact. More generally, we study spaces of invariant sections in homogeneous vector bundles, and take a special interest in the case where the fibres are algebras. These latter cases have a natural global algebra structure. We classify these automorphic algebras for the case where the homogeneous space has compact stabilisers. This work has applications in the theoretical development of geometric deep learning and also in the theory of automorphic Lie algebras.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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