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Numerical Calabi-Yau metrics from holomorphic networks

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arxiv 2012.04797 v2 pith:QWDAJGPS submitted 2020-12-09 hep-th math.CVphysics.comp-ph

Michael R. Douglas , Subramanian Lakshminarasimhan , Yidi Qi This is my paper · ORCID
classification hep-thmath.CVphysics.comp-ph
keywords calabi-yaumethodsmetricsnumericalthemaccurateahlerchoice
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We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\"ahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods.

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Forward citations

Cited by 7 Pith papers

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