The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
This is an incremental pedagogical resource for students in mathematics, physics, and machine-learning, offering no novel findings.
The paper provides an introductory overview of computational geometry, physics, and machine-learning applications related to Calabi-Yau manifolds, aimed at educating students across disciplines without presenting new research results.
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.