Calabi-Yau Metrics, Energy Functionals and Machine-Learning

• URL: http://arxiv.org/abs/2112.10872v1
• Date: Mon, 20 Dec 2021 21:30:06 GMT
• Title: Calabi-Yau Metrics, Energy Functionals and Machine-Learning
• Authors: Anthony Ashmore, Lucille Calmon, Yang-Hui He, Burt A. Ovrut
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 K\"ahler potential of a Calabi-Yau metric having seen only a small sample of training data.

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