Abstract
Recent work uses machine learning to discover relations between knot invariants by connecting numerical experiments with Chern-Simons theory and higher-dimensional gauge theories to derive analytic results.
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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.
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