Cargando…
Machine Learning of Calabi-Yau Volumes
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base manifolds of noncompact toric Calabi-Yau three-folds. We find that the minimum volume can be approximated via a second-order multiple linear regression on standard topological quantities obtained from the...
Autores principales: | Krefl, Daniel, Seong, Rak-Kyeong |
---|---|
Lenguaje: | eng |
Publicado: |
2017
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1103/PhysRevD.96.066014 http://cds.cern.ch/record/2268926 |
Ejemplares similares
-
Counting Calabi-Yau Threefolds
por: Gendler, Naomi, et al.
Publicado: (2023) -
Classifying Calabi-Yau threefolds using infinite distance limits
por: Grimm, Thomas W., et al.
Publicado: (2019) -
The Real Topological String on a local Calabi-Yau
por: Krefl, Daniel, et al.
Publicado: (2009) -
Machine Learning and Algebraic Approaches towards Complete Matter Spectra in 4d F-theory
por: Bies, Martin, et al.
Publicado: (2020) -
Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning
por: Anderson, Lara B., et al.
Publicado: (2020)