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We review briefly the characteristic topological data of Calabi–Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data. This provides an interesting test case for machine learning methodology in discrete mathematics problems motivated by physics.

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Identifying Equivalent Calabi–Yau Topologies: A Discrete Challenge from Math and Physics for Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Vishnu Jejjala,
  • Washington Taylor,
  • Andrew P. Turner

摘要

We review briefly the characteristic topological data of Calabi–Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data. This provides an interesting test case for machine learning methodology in discrete mathematics problems motivated by physics.