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arXiv 1809.02547hep-th
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Toric簇中超曲面的线丛上同调

Machine Learning Line Bundle Cohomologies of Hypersurfaces in Toric Varieties

Daniel Klaewer, Lorenz Schlechter

摘要:

从机器学习不同的技术被应用到计算(超曲面)复曲面簇的线丛上同调的问题。虽然训练神经网络以再现上同调的天真方法在一般情况下失败,但通过检查数据的底层函数形式,我们提出了第二种方法。上同调以分段多项式的方式依赖于线丛荷。我们使用无监督学习来分离不同的多项式相位。其结果是上同调的一个解析公式。这可以变成一个算法,用于计算任意(超曲面中)复曲面簇的解析表达式。

英文摘要:

Different techniques from machine learning are applied to the problem of computing line bundle cohomologies of (hypersurfaces in) toric varieties. While a naive approach of training a neural network to reproduce the cohomologies fails in the general case, by inspecting the underlying functional form of the data we propose a second approach. The cohomologies depend in a piecewise polynomial way on the line bundle charges. We use unsupervised learning to separate the different polynomial phases. The result is an analytic formula for the cohomologies. This can be turned into an algorithm for computing analytic expressions for arbitrary (hypersurfaces in) toric varieties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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