Derives Schwinger-Dyson equations and Ward identities in NN-FT to study anomalies in QFTs via a conserved parameter-space current, yielding a new perspective on symmetries.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
hep-th 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Neural network field theory extended with discrete topological labels recovers the BKT transition and bosonic string T-duality.
α=0 architecture in NNFT minimizes finite-width variance, removes IR corrections, and sets a fundamental SNR bound for correlation functions in scalar field theory.
Neural networks trained on crossing symmetry accurately reconstruct conformal correlators from minimal inputs due to alignment between their spectral bias and CFT smoothness.
citing papers explorer
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Anomalies in Neural Network Field Theory
Derives Schwinger-Dyson equations and Ward identities in NN-FT to study anomalies in QFTs via a conserved parameter-space current, yielding a new perspective on symmetries.
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Topological Effects in Neural Network Field Theory
Neural network field theory extended with discrete topological labels recovers the BKT transition and bosonic string T-duality.
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Optimal Architecture and Fundamental Bounds in Neural Network Field Theory
α=0 architecture in NNFT minimizes finite-width variance, removes IR corrections, and sets a fundamental SNR bound for correlation functions in scalar field theory.
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Neural Networks Reveal a Universal Bias in Conformal Correlators
Neural networks trained on crossing symmetry accurately reconstruct conformal correlators from minimal inputs due to alignment between their spectral bias and CFT smoothness.