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Research PaperResearchia:202604.03016[Data Science > Machine Learning]

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, 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

Submission:4/3/2026
Comments:0 comments
Subjects:Machine Learning; Data Science
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arXiv: This paper is hosted on arXiv, an open-access repository
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Topological Effects in Neural Network Field Theory | Researchia