Title: Adaptive Geometric Framework for Non-Local Computations

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces an innovative self-adaptive geometric framework for non-local computations. Traditional approaches often rely on fixed geometric parameters, which struggle to accurately capture the complex, non-local correlations inherent in many real-world problems. This work proposes a novel method for dynamically adjusting geometric properties to optimize the computation, leading to improved accuracy and efficiency. The framework employs a self-tuning geometry that adapts to the specific characteristics of the data being processed. We demonstrate the effectiveness of this approach through a series of numerical experiments, showcasing significant improvements over traditional methods in a range of applications. The core claim centers on the design of a self-adaptive geometric framework that dynamically adjusts geometry to accurately model non-locality.

DOI: 10.5281/zenodo.22279999

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