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Hilbert series are a standard tool in algebraic geometry, and more recently are finding many uses in theoretical physics. This summary reviews work applying machine learning to databases of Hilbert series to determine geometric properties of the underlying algebraic variety; and was prepared for the proceedings of the Nankai Symposium on Mathematical Dialogues, 2021.

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Machine Learning for Hilbert Series

  • 会议论文

  • 发表日期：

    2026年

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  • Edward Hirst

摘要

Hilbert series are a standard tool in algebraic geometry, and more recently are finding many uses in theoretical physics. This summary reviews work applying machine learning to databases of Hilbert series to determine geometric properties of the underlying algebraic variety; and was prepared for the proceedings of the Nankai Symposium on Mathematical Dialogues, 2021.