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• W3048796016 abstract "We apply machine learning to the problem of finding numerical Calabi-Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on Kahler manifolds, we combine conventional curve fitting and machine-learning techniques to numerically approximate Ricci-flat metrics. We show that machine learning is able to predict the Calabi-Yau metric and quantities associated with it, such as its determinant, having seen only a small sample of training data. Using this in conjunction with a straightforward curve fitting routine, we demonstrate that it is possible to find highly accurate numerical metrics much more quickly than by using Donaldson's algorithm alone, with our new machine-learning algorithm decreasing the time required by between one and two orders of magnitude." @default.
• W3048796016 created "2020-08-18" @default.
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• W3048796016 date "2020-08-12" @default.
• W3048796016 modified "2023-10-13" @default.
• W3048796016 title "Machine Learning Calabi–Yau Metrics" @default.
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• W3048796016 doi "https://doi.org/10.1002/prop.202000068" @default.
• W3048796016 hasPublicationYear "2020" @default.
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