Hilbert series, machine learning, and applications to physics-Reference-Cited by-同舟云学术

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Hilbert series, machine learning, and applications to physics

Published:2022-04 Issue: Volume:827 Page:136966
ISSN:0370-2693
Container-title:Physics Letters B
language:en
Short-container-title:Physics Letters B

Author:

Bao Jiakang^ORCID,He Yang-Hui^ORCID,Hirst Edward^ORCID,Hofscheier Johannes^ORCID,Kasprzyk Alexander,Majumder Suvajit^ORCID

Publisher

Elsevier BV

Subject

Nuclear and High Energy Physics

Reference62 articles.

1. Counting BPS operators in gauge theories: quivers, syzygies and plethystics;Benvenuti;J. High Energy Phys.,2007

2. Counting gauge invariants: the plethystic program;Feng;J. High Energy Phys.,2007

3. SQCD: a geometric aperçu;Gray;J. High Energy Phys.,2008

4. The Hilbert series of adjoint SQCD;Hanany;Nucl. Phys. B,2010

5. The Hilbert series of U/SU SQCD and Toeplitz determinants;Chen;Nucl. Phys. B,2011

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1. Machine learning BPS spectra and the gap conjecture;Physical Review D;2024-08-16

2. AI-driven research in pure mathematics and theoretical physics;Nature Reviews Physics;2024-08-05

3. Machine Learning Clifford Invariants of ADE Coxeter Elements;Advances in Applied Clifford Algebras;2024-05-04

4. New Calabi–Yau manifolds from genetic algorithms;International Journal of Data Science in the Mathematical Sciences;2024-05-04

5. Calabi-Yau four-, five-, sixfolds as Pwn hypersurfaces: Machine learning, approximation, and generation;Physical Review D;2024-05-02

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