REVIEW 4 cited by
The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 4 Pith papers
-
Machine Learning Free Quotients of CICYs
Machine-learning classifiers, especially a multi-head attention model, correctly identify almost all free Z2, Z3, Z4, and Z2xZ2 quotients of CICYs on held-out manifolds, with only three missed Z2xZ2 cases.
-
BPS spectroscopy with reinforcement learning
A PPO reinforcement learning agent finds quiver mutation sequences that enumerate finite BPS spectra and minimal chambers of complete N=2 theories, with new chamber counts for SU(2) Nf=4.
-
Pre-Strings Lectures on Artificial Intelligence
Lecture notes define neural-network field theory and survey how it recovers known QFT/string results plus applied AI techniques for string problems.
-
Ricci-Flat Mirror Hypersurfaces in Spaces of General Type
Laurent-polynomial anticanonical sections can smooth Calabi-Yau hypersurfaces in general-type toric/torus manifolds and yield transposition mirrors with computable GLSM data.
Discussion (0). Continue with ORCID to comment.