(K)not machine learning
High Energy Physics - Theory
2022-01-24 v1 Geometric Topology
Abstract
We review recent efforts to machine learn relations between knot invariants. Because these knot invariants have meaning in physics, we explore aspects of Chern-Simons theory and higher dimensional gauge theories. The goal of this work is to translate numerical experiments with Big Data to new analytic results.
Cite
@article{arxiv.2201.08846,
title = {(K)not machine learning},
author = {Jessica Craven and Mark Hughes and Vishnu Jejjala and Arjun Kar},
journal= {arXiv preprint arXiv:2201.08846},
year = {2022}
}
Comments
10 pages, 2 figures, LaTeX, based on a talk given by VJ at the Nankai Symposium on Mathematical Dialogues, August 2021