mlsanityData to 14 Oct 2026

Calabi-Yau Metrics, Energy Functionals and Machine-Learning

arXiv 2112.10872 ↗First posted 20 Dec 2021hep-th cs.LG math.AGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 5 of its 43 references that are in the map. First posted on arXiv 20 Dec 2021.

Builds on

5 of 43 references.

References · 5

  1. 2020Neural Network Approximations for Calabi-Yau Metricscited by 32
  2. 2021Learning Size and Shape of Calabi-Yau Spacescited by 28
  3. 2019Machine learning Calabi-Yau metricscited by 23
  4. 2017Deep-Learning the Landscapecited by 21
  5. 2018The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learningcited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2024A Triumvirate of AI Driven Theoretical Discoverycited by 5
  2. 2023The R-mAtrIx Netcited by 4
  3. 2024Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithmcited by 4
  4. 2026Neural and numerical methods for G2-structures on contact Calabi-Yau 7-manifoldscited by 4
  5. 2024Generative AI for Brane Configurations and Coamoebacited by 2
  6. 2025Approximate Ricci-flat Metrics for Calabi-Yau Manifoldscited by 1
  7. 2022Characterizing 4-string contact interaction using machine learningcited by 0
  8. 2023Calabi-Yau Four/Five/Six-folds as mathbbPnw Hypersurfaces: Machine Learning, Approximation, and Generationcited by 0
  9. 2025Machine Learning the 6d Supergravity Landscapecited by 0