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New interpolation method uses diffusion and Voronoi tessellations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel data-driven interpolation framework designed to reconstruct real-valued functions on smooth manifolds from scattered data points. This method integrates a Gaussian kernel interpolant with a Voronoi-adaptive bandwidth, which is determined by the data's geometry. The approach offers a closed-form solution that requires no training, iterative optimization, or parameter tuning, and it can be computed efficiently with linear complexity relative to the number of sample points. AI

IMPACT This research introduces a new mathematical framework for data interpolation, potentially improving the accuracy and efficiency of function reconstruction in various scientific and engineering applications.

RANK_REASON The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New interpolation method uses diffusion and Voronoi tessellations

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The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alvaro Almeida Gomez ·

    A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations

    arXiv:2509.03758v5 Announce Type: replace Abstract: We propose a data-driven interpolation framework for reconstructing real-valued functions on smooth manifolds from scattered pointwise observations. The method combines a Gaussian Nadaraya--Watson kernel interpolant with a Voron…

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