Machine Learning Calabi–Yau Metrics-Reference-Cited by-同舟云学术

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Machine Learning Calabi–Yau Metrics

Published:2020-08-12 Issue:9 Volume:68 Page:2000068
ISSN:0015-8208
Container-title:Fortschritte der Physik
language:en
Short-container-title:Fortschr. Phys.

Author:

Ashmore Anthony¹^ORCID,He Yang‐Hui²³⁴^ORCID,Ovrut Burt A.¹^ORCID

Affiliation:

1. Department of Physics University of Pennsylvania Philadelphia PA 19104 USA

2. Merton College University of Oxford OX1 4JD UK

3. Department of Mathematics City University of London EC1V 0HB UK

4. School of Physics NanKai University Tianjin 300071 P.R. China

Funder

Science and Technology Facilities Council

U.S. Department of Energy

Publisher

Wiley

Subject

General Physics and Astronomy

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/prop.202000068

Reference74 articles.

1. Vacuum configurations for superstrings

2. A heterotic standard model

3. An SU(5) heterotic standard model

4. The exact MSSM spectrum from string theory

5. A Standard Model from theE8×E8heterotic superstring

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI-driven research in pure mathematics and theoretical physics;Nature Reviews Physics;2024-08-05

2. Calabi-Yau four-, five-, sixfolds as Pwn hypersurfaces: Machine learning, approximation, and generation;Physical Review D;2024-05-02

3. Machine learning Sasakian and G2 topology on contact Calabi-Yau 7-manifolds;Physics Letters B;2024-03

4. The world in a grain of sand: Condensing the string vacuum degeneracy;Physics Letters B;2024-02

5. An autoencoder for heterotic orbifolds with arbitrary geometry;Journal of Physics Communications;2024-02-01

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