Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning

DOI10.1007/JHEP05(2021)013zbMath1466.83111arXiv2012.04656OpenAlexW3158826103MaRDI QIDQ2047897

Sven Krippendorf, Fabian Ruehle, Mathis Gerdes, Nikhil Raghuram, Lara B. Anderson, James A. Gray

Publication date: 4 August 2021

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.04656

zbMATH Keywords

superstring vacuasuperstrings and heterotic stringsdifferential and algebraic geometry

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) String and superstring theories in gravitational theory (83E30) Calabi-Yau theory (complex-analytic aspects) (32Q25) Effective quantum field theories (81T12)

Related Items

Machine learning line bundle connections, Brain webs for brane webs, Numerical spectra of the Laplacian for line bundles on Calabi-Yau hypersurfaces, Black holes and the loss landscape in machine learning, Neural network approximations for Calabi-Yau metrics, On the Search for Low W₀, Level crossings, attractor points and complex multiplication, From the string landscape to the mathematical landscape: a machine-learning outlook, Generalized vanishing theorems for Yukawa couplings in heterotic compactifications, Yukawa textures from singular spectral data, Calabi-Yau CFTs and random matrices

Uses Software

• SymPy
• TensorFlow
• PyTorch
• Adam
• GitHub
• JAX
• Fermat.m

Cites Work

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