Machine learning line bundle connections

被引：5

作者：

Ashmore, Anthony ^{[1
,2
]}

Deen, Rehan ^{[3
]}

He, Yang-Hui ^{[4
,5
,6
,7
]}

Ovrut, Burt A. ^{[8
]}

机构：

[1] Univ Chicago, Kadanoff Ctr Theoret Phys, Chicago, IL 60637 USA

[2] Sorbonne Univ, CNRS, LPTHE, F-75005 Paris, France

[3] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Oxford OX1 3PU, England

[4] Royal Inst Great Britain, London Inst Math Sci, London W1S 4BS, England

[5] Univ London, Dept Math, London EC1V0HB, England

[6] Univ Oxford, Merton Coll, Oxford OX1 4JD, England

[7] NanKai Univ, Sch Phys, Tianjin 300071, Peoples R China

[8] Univ Penn, Dept Phys, Philadelphia, PA 19104 USA

来源：

PHYSICS LETTERS B | 2022年 / 827卷

基金：

欧盟地平线“2020”;

关键词：

Yang-Mills; Machine learning; Connections; 3-GENERATION SUPERSTRING MODEL; YANG-MILLS CONNECTIONS; MINI-LANDSCAPE; METRICS; VACUA;

D O I：

10.1016/j.physletb.2022.136972

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We study the use of machine learning for finding numerical hermitian Yang-Mills connections on line bundles over Calabi-Yau manifolds. Defining an appropriate loss function and focusing on the examples of an elliptic curve, a K3 surface and a quintic threefold, we show that neural networks can be trained to give a close approximation to hermitian Yang-Mills connections. (C) 2022 The Author(s). Published by Elsevier B.V.& nbsp;

引用

页数：9

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