Machine learning Calabi-Yau metrics

Machine learning Calabi-Yau metrics with JAX

Surface geometry plugin for Rhinoceros 3D

Machine Learning for CICY 3-folds

An implementation of the controlled reduction method for computing the Hasse-Weil zeta functions of smooth projective hypersurfaces over finite fields

Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces (arXiv: 2112.06350).

Tools for Calabi-Yau hypersurfaces in normal toric varieties associated to triangulations of reflexive polytopes in the Kreuzer-Skarke lists.

Generation of Calabi-Yau links from wp4 spaces, computation of their topological properties (Sasakian Hodge numbers, CN invariant), and their ML.

Computing Gopakumar-Vafa invariants on Quintic three-fold

Weight systems of 6 weights, defining weighted P5s and CY4s, are studied with ML. An approximation of Hodge computation is presented and used to generate transverse weight systems for CY5s and CY6s (arXiv: 2311.17146).

Repository for arxiv:2311.17146.

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