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arXiv 2009.02544hep-thmath.AGstat.ML
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机器学习Calabi-Yau四重

Machine Learning Calabi-Yau Four-folds

Yang-Hui He, Andre Lukas

摘要:

卡-丘流形的霍奇数依赖于底层流形数据,这对机器学习提出了一个有趣的挑战。在这封信中,我们考虑了完全相交的卡-丘四重流形的数据集,一组大约90万个拓扑类型,并研究了这些流形的霍奇数h^1,1和h^3,1的监督学习。我们发现h^1,1可以通过完全连接的分类器和回归器网络成功学习(达到96%的精度)。虽然这两种类型的网络对h^3,1都失败了,但我们证明了一个更复杂的双分支网络,结合特征增强,可以作为h^3,1的有效回归器(精度达到98%),至少对于数据的一个子集。这暗示了一个至今未知的霍奇数公式的存在。

英文摘要:

Hodge numbers of Calabi-Yau manifolds depend non-trivially on the underlying manifold data and they present an interesting challenge for machine learning. In this letter we consider the data set of complete intersection Calabi-Yau four-folds, a set of about 900,000 topological types, and study supervised learning of the Hodge numbers h^1,1 and h^3,1 for these manifolds. We find that h^1,1 can be successfully learned (to 96% precision) by fully connected classifier and regressor networks. While both types of networks fail for h^3,1, we show that a more complicated two-branch network, combined with feature enhancement, can act as an efficient regressor (to 98% precision) for h^3,1, at least for a subset of the data. This hints at the existence of an, as yet unknown, formula for Hodge numbers.

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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