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Research PaperResearchia:202604.03016

Topological Effects in Neural Network Field Theory

Christian Ferko

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, sho...

Submitted: April 3, 2026Subjects: Machine Learning; Data Science

Description / Details

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.


Source: arXiv:2604.02313v1 - http://arxiv.org/abs/2604.02313v1 PDF: https://arxiv.org/pdf/2604.02313v1 Original Link: http://arxiv.org/abs/2604.02313v1

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Date:
Apr 3, 2026
Topic:
Data Science
Area:
Machine Learning
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