@misc{gerdes_cyjax_2022,
	title = {{CYJAX}: {A} package for {Calabi}-{Yau} metrics with {JAX}},
	shorttitle = {{CYJAX}},
	url = {http://arxiv.org/abs/2211.12520},
	doi = {10.48550/arXiv.2211.12520},
	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 K{\textbackslash}"ahler potential which automatically satisfies K{\textbackslash}"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.},
	urldate = {2022-11-28},
	publisher = {arXiv},
	author = {Gerdes, Mathis and Krippendorf, Sven},
	month = nov,
	year = {2022},
	note = {arXiv:2211.12520 [hep-th]},
	keywords = {Calabi-Yau metrics, automatic differentiation, high energy physics, machine learning, mentions sympy},
}

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