We present the first version of CYJAX, a package for machine learning Calabi-Yau metrics using JAX. It is meant to be accessible both as a top-level tool and as a library of modular functions. CYJAX is currently centered around the algebraic ansatz for the K\"ahler potential which automatically satisfies K\"ahlerity and compatibility on patch overlaps. As of now, this implementation is limited to varieties defined by a single defining equation on one complex projective space. We comment on some planned generalizations.
@article{arxiv.2211.12520,
title = {CYJAX: A package for Calabi-Yau metrics with JAX},
author = {Mathis Gerdes and Sven Krippendorf},
journal= {arXiv preprint arXiv:2211.12520},
year = {2023}
}
Comments
17 pages, 5 figures; minor corrections of code examples & clarifications; documentation at https://cyjax.readthedocs.io and code at https://github.com/ml4physics/cyjax