Lattice gauge equivariant convolutional neural networks

Autor:	Favoni, Matteo, Ipp, Andreas, Müller, David I., Schuh, Daniel
Rok vydání:	2020
Předmět:	High Energy Physics - Lattice Computer Science - Machine Learning High Energy Physics - Phenomenology High Energy Physics - Theory Statistics - Machine Learning
Zdroj:	Phys.Rev.Lett. 128 (2022) 3, 032003
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevLett.128.032003
Popis:	We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems. At the heart of this network structure is a novel convolutional layer that preserves gauge equivariance while forming arbitrarily shaped Wilson loops in successive bilinear layers. Together with topological information, for example from Polyakov loops, such a network can in principle approximate any gauge covariant function on the lattice. We demonstrate that L-CNNs can learn and generalize gauge invariant quantities that traditional convolutional neural networks are incapable of finding. Comment: letter: 6 pages, 5 figures; supplementary material: 14 pages, 4 figures; replaced some figures, added supplementary material
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.12901 Zobrazit plný text záznamu View this record from Arxiv