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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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(K)not Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Jessica Craven,
  • Mark Hughes,
  • Vishnu Jejjala,
  • Arjun Kar

摘要

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.