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arXiv 2411.17331math.GTq-bio.BM
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用于纽结数据分析的多尺度琼斯多项式和持久琼斯多项式

Multiscale Jones Polynomial and Persistent Jones Polynomial for Knot Data Analysis

Ruzhi Song, Fengling Li, Jie Wu, Fengchun Lei, Guo-Wei Wei

AI总结:

针对经典纽结理论缺乏局部结构信息的问题,提出多尺度琼斯多项式和持久琼斯多项式两种局部化模型,分析其对曲线小扰动的稳定性,为纽结数据分析提供鲁棒的理论工具。

摘要:

科学、工程和艺术中的许多结构可被视为三维空间中的曲线。这些曲线的纠缠对材料的功能和物理性质起着关键作用。纽结理论中的诸多概念为探索三维空间中曲线的复杂性和纠缠提供了理论工具。然而,经典纽结理论主要关注全局拓扑性质,缺乏对局部结构信息的考虑,而局部结构信息在实际应用中至关重要。本研究提出了两种基于琼斯多项式的局部化模型,即多尺度琼斯多项式和持久琼斯多项式。分析了这些模型的稳定性,特别是多尺度和持久琼斯多项式模型对曲线集合中小扰动的不敏感性,从而确保了它们在实际应用中的鲁棒性。

英文摘要:

Many structures in science, engineering, and art can be viewed as curves in 3-space. The entanglement of these curves plays a crucial role in determining the functionality and physical properties of materials. Many concepts in knot theory provide theoretical tools to explore the complexity and entanglement of curves in 3-space. However, classical knot theory primarily focuses on global topological properties and lacks the consideration of local structural information, which is critical in practical applications. In this work, two localized models based on the Jones polynomial, namely the multiscale Jones polynomial and the persistent Jones polynomial, are proposed. The stability of these models, especially the insensitivity of the multiscale and persistent Jones polynomial models to small perturbations in curve collections, is analyzed, thus ensuring their robustness for real-world applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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