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  • Open Access

SU(N) lattice gauge theories with physics-informed neural networks

Simone Romiti*

  • Institute for Theoretical Physics, Albert Einstein Center for Fundamental Physics, University of Bern, CH-3012 Bern, Switzerland

  • *Contact author: simone.romiti.1994@gmail.com
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Phys. Rev. D 113, 054511 – Published 26 March, 2026

DOI: https://doi.org/10.1103/mb67-9rkf

Abstract

We present an application of physics-informed neural networks (PINNs) to the study of SU(Nc) 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 toward 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 U(1) and SU(2) pure-gauge theories, showing that the PINNs successfully reproduce the hierarchy of energy levels and their corresponding wave functions.

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Phys. Rev. D 113, 054511– Published 26 March, 2026
  • Received 18 December 2025
  • Accepted 20 February 2026
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