Machine-learned Calabi-Yau metrics and curvature
type: Item
instance of: scholarly article
title: Machine-learned Calabi-Yau metrics and curvature
zbMATH Open document ID: 1548.53009
author: Per Berglund
author: Giorgi Butbaia
author: T. Hübsch
author: Vishnu Jejjala
author: Damián Kaloni Mayorga Peña
author: Challenger Mishra
author: Justin T. Tan
published in: Advances in Theoretical and Mathematical Physics
publication date: 2024-07-15T00:00:00Z
review text: The authors apply machine learning techniques to investigate numerical Ricci-flat metrics on smooth and singular K3 surfaces and Calabi-Yau threefolds, specifically the Cefalú family of quartic twofolds and the Dwork family of quintic threefolds.\N\NThey study the numerical stability of the numerically computed topological invariants such as the Euler characteristics and the Chern classes, suggesting a different neural network model, called spectral networks. They also apply persistent homology methods.
reviewed by: Daniele Angella
Mathematics Subject Classification ID: 53-08
Mathematics Subject Classification ID: 53C55
Mathematics Subject Classification ID: 14J32
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 55N31
zbMATH DE Number: 7880784
zbMATH Keywords: Calabi-Yau metrics
zbMATH Keywords: neural networks
zbMATH Keywords: persistent homology
MaRDI profile type: Publication
DOI: 10.4310/ATMP.2023.V27.N4.A3
description: scientific article; zbMATH DE number 7880784