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arXiv 2012.15821hep-thcs.LGmath.AGmath.DG

Neural Network Approximations for Calabi-Yau Metrics

Mandelstam Institute for Theoretical Physics(曼德尔斯塔姆理论物理研究所);University of the Witwatersrand(威特沃特斯兰德大学);Universidad de Guanajuato(瓜纳华托大学);University of Cambridge(剑桥大学)

Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra

英文摘要:

Ricci flat metrics for Calabi-Yau threefolds are not known analytically. In this work, we employ techniques from machine learning to deduce numerical flat metrics for the Fermat quintic, for the Dwork quintic, and for the Tian-Yau manifold. This investigation employs a single neural network architecture that is capable of approximating Ricci flat Kaehler metrics for several Calabi-Yau manifolds of dimensions two and three. We show that measures that assess the Ricci flatness of the geometry decrease after training by three orders of magnitude. This is corroborated on the validation set, where the improvement is more modest. Finally, we demonstrate that discrete symmetries of manifolds can be learned in the process of learning the metric.

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