Inception Neural Network for Complete Intersection Calabi-Yau 3-folds

Code

• github.com/thesfinox/ml-cicy
  Officialtf★ 7
• github.com/melsophos/cicy
  none★ 2
• github.com/robin-schneider/cicy-fourfolds
  tf★ 2

Abstract

We introduce a neural network inspired by Google's Inception model to compute the Hodge number h^1,1 of complete intersection Calabi-Yau (CICY) 3-folds. This architecture improves largely the accuracy of the predictions over existing results, giving already 97% of accuracy with just 30% of the data for training. Moreover, accuracy climbs to 99% when using 80% of the data for training. This proves that neural networks are a valuable resource to study geometric aspects in both pure mathematics and string theory.

Reproductions