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• W4226085488 abstract "We apply machine learning to the problem of finding numerical Calabi-Yau metrics. We extend previous work on learning approximate Ricci-flat metrics calculated using Donaldson's algorithm to the much more accurate optimal metrics of Headrick and Nassar. We show that machine learning is able to predict the Kahler potential of a Calabi-Yau metric having seen only a small sample of training data." @default.
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• W4226085488 date "2022-05-21" @default.
• W4226085488 modified "2023-10-14" @default.
• W4226085488 title "Calabi–Yau metrics, energy functionals and machine learning" @default.
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• W4226085488 doi "https://doi.org/10.1142/s2810939222500034" @default.
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