mlsanityData to 14 Oct 2026

cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature

arXiv 2410.19728 ↗First posted 25 Oct 2024hep-th cs.LG hep-phcited by 3

Not on the map. Why?

Abstract (arXiv)

We introduce textttcymyc, a high-performance Python library for numerical investigation of the geometry of a large class of string compactification manifolds and their associated moduli spaces. We develop a well-defined geometric ansatz to numerically model tensor fields of arbitrary degree on a large class of Calabi-Yau manifolds. textttcymyc includes a machine learning component which incorporates this ansatz to model tensor fields of interest on these spaces by finding an approximate solution to the system of partial differential equations they should satisfy.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 55 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

4 of 55 references.

References · 4

  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. 2022Machine Learned Calabi-Yau Metrics and Curvaturecited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.