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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representative 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.
Simple neural networks trained on crossing symmetry and one anchor point reproduce conformal correlators to within a few percent across many CFTs.
The paper introduces a formalism for constructing conformally invariant defects in Neural Network Field Theories, demonstrates it on two toy scalar models, and provides a neural-network reading of a defect OPE expansion in two-point functions.
Lecture notes that treat statistical physics as probability theory and connect Ising models, spin glasses, and renormalization group ideas to Hopfield networks, restricted Boltzmann machines, and large language models.
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
Simple neural networks trained on crossing symmetry and one anchor point reproduce conformal correlators to within a few percent across many CFTs.
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Conformal Defects in Neural Network Field Theories
The paper introduces a formalism for constructing conformally invariant defects in Neural Network Field Theories, demonstrates it on two toy scalar models, and provides a neural-network reading of a defect OPE expansion in two-point functions.
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Lecture Notes on Statistical Physics and Neural Networks
Lecture notes that treat statistical physics as probability theory and connect Ising models, spin glasses, and renormalization group ideas to Hopfield networks, restricted Boltzmann machines, and large language models.