Approximate and discrete Euclidean vector bundles
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Abstract (arXiv)
We introduce varepsilon-approximate versions of the notion of Euclidean vector bundle for varepsilon ≥ 0, which recover the classical notion of Euclidean vector bundle when varepsilon = 0. In particular, we study vCech cochains with coefficients in the orthogonal group that satisfy an approximate cocycle condition. We show that varepsilon-approximate vector bundles can be used to represent classical vector bundles when varepsilon > 0 is sufficiently small. We also introduce distances between approximate vector bundles and use them to prove that sufficiently similar approximate vector bundles represent the same classical vector bundle. This gives a way of specifying vector bundles over finite simplicial complexes using a finite amount of data, and also allows for some tolerance to noise when working with vector bundles in an applied setting. As an example, we prove a reconstruction theorem for vector bundles from finite samples. We give algorithms for the effective computation of low-dimensional characteristic classes of vector bundles directly from discrete and approximate representations and illustrate the usage of these algorithms with computational examples.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
7 papers.
- 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
- 2021Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Mapscited by 7
- 2022FibeRed: Fiberwise Dimensionality Reduction of Topologically Complex Data with Vector Bundlescited by 7
- 2022Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Mapscited by 2
- 2023O(k)-Equivariant Dimensionality Reduction on Stiefel Manifoldscited by 1
- 2023Windowed Fourier Analysis for Signal Processing on Graph Bundlescited by 0
- 2026Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlasescited by 0
- 2026Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases26 Feb 2026
- 2023O(k)-Equivariant Dimensionality Reduction on Stiefel Manifolds19 Sep 2023
- 2023Windowed Fourier Analysis for Signal Processing on Graph Bundles11 Feb 2023
- 2022FibeRed: Fiberwise Dimensionality Reduction of Topologically Complex Data with Vector Bundles13 Jun 2022
- 2022Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps15 Mar 2022
- 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs9 Feb 2022
- 2021Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps13 Oct 2021