Getting CICY high

doi:10.1016/j.physletb.2019.06.067

Getting CICY high

2019-08-01

36

OA

AI

DOI：10.1016/j.physletb.2019.06.067

原文链接

查看原文

摘要

En 中文

Supervised machine learning can be used to predict properties of string geometries with previously unknown features. Using the complete intersection Calabi-Yau (CICY) threefold dataset as a theoretical laboratory for this investigation, we use low h(1,1) geometries for training and validate on geometries with large h(1,1). Neural networks and Support Vector Machines successfully predict trends in the number of Kohler parameters of CICY threefolds. The numerical accuracy of machine learning improves upon seeding the training set with a small number of samples at higher h(1,1). (C) 2019 The Authors. Published by Elsevier B.V.

Keyword:

Machine learning

Neural network

Support Vector Machine

Calabi-Yau

String compactifications

Getting CICY high

摘要

AI总结

期刊

机构