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Calabi-Yau Metrics, Energy Functionals and Machine-Learning
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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 K\"ahler potential of a Calabi-Yau metric having seen only a small sample of training data.
Forward citations
Cited by 3 Pith papers
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Approximate Ricci-flat Metrics for Calabi-Yau Manifolds
Analytic approximate Ricci-flat Kähler potentials are obtained for one-parameter Dwork quintic and bi-cubic Calabi-Yau three-folds by fitting Donaldson's Ansatz to machine-learned numerical metrics.
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Machine Learning the 6d Supergravity Landscape
An autoencoder and two neural classifiers, trained only on anomaly Gram matrices, provide automated clustering, outlier detection, and consistency predictions for millions of 6d supergravity building blocks.
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What to do with a Ricci-flat Calabi--Yau metric?
Numerical Ricci-flat Calabi–Yau metrics turn string compactifications from topological existence statements into computable geometries, unlocking normalized couplings, spectra, and metric-level tests of mirror symmetry.
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