CYJAX: A package for Calabi-Yau metrics with JAX

Abstract

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 Kähler potential which automatically satisfies Kählerity 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.

arXiv comment17 pages, 5 figures; minor corrections of code examples & clarifications; documentation at https://cyjax.readthedocs.io and code at https://github.com/ml4physics/cyjax

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• cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature
• AInstein: Numerical Einstein Metrics via Machine Learning