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High Energy Physics - Theory

arXiv:1812.02893 (hep-th)
[Submitted on 7 Dec 2018 (v1), last revised 20 Dec 2020 (this version, v2)]

Title:The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning

Authors:Yang-Hui He
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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.
Comments: book to appear with Springer; v1 #pages = #irreps(Monster), 44 figures; v2 substantially expanded: about 100 extra pages and 10 figures added
Subjects: High Energy Physics - Theory (hep-th); Mathematical Physics (math-ph); Algebraic Geometry (math.AG); Machine Learning (stat.ML)
Cite as: arXiv:1812.02893 [hep-th]
  (or arXiv:1812.02893v2 [hep-th] for this version)
  https://doi.org/10.48550/arXiv.1812.02893

Submission history

From: Yang-Hui He [view email]
[v1] Fri, 7 Dec 2018 03:45:27 UTC (1,937 KB)
[v2] Sun, 20 Dec 2020 18:47:49 UTC (2,854 KB)
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