Calabi-Yau Metrics, Energy Functionals and Machine-Learning

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 Kähler potential of a Calabi-Yau metric having seen only a small sample of training data.

7 pages, 5 figures

References in corpus (4)

• Machine Learning of Calabi-Yau Volumes
• Machine Learning in the String Landscape
• Evolving neural networks with genetic algorithms to study the String Landscape
• Numerical Calabi-Yau metrics

Cited by in corpus (1)

• Characterizing 4-string contact interaction using machine learning