$\mathrm{SU(N)}$ lattice gauge theories with Physics-Informed Neural Networks

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1. Verfasser: Romiti, Simone
Format: Preprint
Veröffentlicht: 2025
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author Romiti, Simone
author_facet Romiti, Simone
contents We present an application of Physics-Informed Neural Networks (PINNs) to the study of $\mathrm{SU}(N_c)$ lattice gauge theories. Our method enables the learning of eigenfunctions and eigenvalues at arbitrary gauge couplings, smoothly moving from the analytically known strong-coupling regime towards weaker couplings. By encoding the Schrödinger equation and the symmetries of the eigenstates directly into the loss function, the network performs an unsupervised exploration of the spectrum. We validate the approach on the single-plaquette $\mathrm{U}(1)$ and $\mathrm{SU}(2)$ pure-gauge theories, showing that the PINNs successfully reproduce the hierarchy of energy levels and their corresponding wavefunctions.
format Preprint
id arxiv_https___arxiv_org_abs_2510_26904
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle $\mathrm{SU(N)}$ lattice gauge theories with Physics-Informed Neural Networks
Romiti, Simone
High Energy Physics - Lattice
We present an application of Physics-Informed Neural Networks (PINNs) to the study of $\mathrm{SU}(N_c)$ lattice gauge theories. Our method enables the learning of eigenfunctions and eigenvalues at arbitrary gauge couplings, smoothly moving from the analytically known strong-coupling regime towards weaker couplings. By encoding the Schrödinger equation and the symmetries of the eigenstates directly into the loss function, the network performs an unsupervised exploration of the spectrum. We validate the approach on the single-plaquette $\mathrm{U}(1)$ and $\mathrm{SU}(2)$ pure-gauge theories, showing that the PINNs successfully reproduce the hierarchy of energy levels and their corresponding wavefunctions.
title $\mathrm{SU(N)}$ lattice gauge theories with Physics-Informed Neural Networks
topic High Energy Physics - Lattice
url https://arxiv.org/abs/2510.26904