Data Driven Perspectives on Knot Theory

Pawel Dlotko; Davide Gurnari; Radmila Sazdanovic

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arXiv · 2503.15103

Data Driven Perspectives on Knot Theory

Pawel Dlotko·Davide Gurnari·Radmila Sazdanovic

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Abstract

Data science offers a powerful tool to understand objects in multiple sciences. In this paper we utilize concept of data science, most notably topological data analysis, to extend our understanding of knot theory. This approach provides a way to extend mathematical exposition of various invariants of knots towards understanding their relations in statistical and cumulative way. Paper included examples illustrating how topological data analysis can illuminate structure and relations between knot invariants, state new hypothesis, and gain new insides into long standing conjectures.

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Pawel Dlotko, Davide Gurnari, Radmila Sazdanovic. 2025-03-19. Data Driven Perspectives on Knot Theory. https://arxiv.org/abs/2503.15103

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