Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

Autor:	Gerdes, Mathis, de Haan, Pim, Rainone, Corrado, Bondesan, Roberto, Cheng, Miranda C. N.
Rok vydání:	2022
Předmět:	High Energy Physics - Lattice Condensed Matter - Statistical Mechanics Computer Science - Machine Learning High Energy Physics - Theory
Zdroj:	SciPost Phys. 15, 238 (2023)
Druh dokumentu:	Working Paper
DOI:	10.21468/SciPostPhys.15.6.238
Popis:	We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the $\phi^4$ theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods. Comment: 17 pages, 9 figures, 1 table; slightly expanded published version, added 2 figures and 2 sections to appendix
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.00283 Zobrazit plný text záznamu View this record from Arxiv