TL;DR
This paper develops a spectral method for exterior calculus in manifold learning, enabling accurate computation of topological features and differential operators from data with reduced memory use and convergence guarantees.
Contribution
It introduces a spectral reformulation of exterior calculus using Laplacian eigenfunctions, providing convergence results and practical algorithms for data-driven topology and geometry analysis.
Findings
Accurate eigenvalue and eigenform recovery demonstrated
Betti numbers correctly identified from sampled data
Effective vector field visualization via eigenforms
Abstract
A spectral approach to building the exterior calculus in manifold learning problems is developed. The spectral approach is shown to converge to the true exterior calculus in the limit of large data. Simultaneously, the spectral approach decouples the memory requirements from the amount of data points and ambient space dimension. To achieve this, the exterior calculus is reformulated entirely in terms of the eigenvalues and eigenfunctions of the Laplacian operator on functions. The exterior derivatives of these eigenfunctions (and their wedge products) are shown to form a frame (a type of spanning set) for appropriate spaces of -forms, as well as higher-order Sobolev spaces. Formulas are derived to express the Laplace-de Rham operators on forms in terms of the eigenfunctions and eigenvalues of the Laplacian on functions. By representing the Laplace-de Rham operators in this…
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