The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
arXiv:1812.02893
Abstract
We present a pedagogical introduction to the recent advances in the computational geometry, physical implications, and data science of Calabi-Yau manifolds. Aimed at the beginning research student and using Calabi-Yau spaces as an exciting play-ground, we intend to teach some mathematics to the budding physicist, some physics to the budding mathematician, and some machine-learning to both. Based on various lecture series, colloquia and seminars given by the author in the past year, this writing is a very preliminary draft of a book to appear with Springer, by whose kind permission we post to ArXiv for comments and suggestions.
arXiv commentbook to appear with Springer; v1 #pages = #irreps(Monster), 44 figures; v2 substantially expanded: about 100 extra pages and 10 figures added
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Cited by in corpus (28)
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- Getting CICY High
- Quiver Mutations, Seiberg Duality and Machine Learning
- Machine Learning Calabi-Yau Four-folds
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