English

Topological Effects in Neural Network Field Theory

High Energy Physics - Theory 2026-04-06 v1 Disordered Systems and Neural Networks Machine Learning

Abstract

Neural network field theory formulates field theory as a statistical ensemble of fields defined by a network architecture and a density on its parameters. We extend the construction to topological settings via the inclusion of discrete parameters that label the topological quantum number. We recover the Berezinskii--Kosterlitz--Thouless transition, including the spin-wave critical line and the proliferation of vortices at high temperatures. We also verify the T-duality of the bosonic string, showing invariance under the exchange of momentum and winding on S1S^1, the transformation of the sigma model couplings according to the Buscher rules on constant toroidal backgrounds, the enhancement of the current algebra at self-dual radius, and non-geometric T-fold transition functions.

Keywords

Cite

@article{arxiv.2604.02313,
  title  = {Topological Effects in Neural Network Field Theory},
  author = {Christian Ferko and James Halverson and Vishnu Jejjala and Brandon Robinson},
  journal= {arXiv preprint arXiv:2604.02313},
  year   = {2026}
}

Comments

55 pages, 8 figures

R2 v1 2026-07-01T11:51:35.971Z