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Solving Conformal Field Theories with Artificial Intelligence

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arxiv 2108.08859 v2 pith:ERXU3QU7 submitted 2021-08-19 hep-th

Gergely Kántor , Vasilis Niarchos , Constantinos Papageorgakis This is my paper · ORCID
classification hep-th
keywords cftstheoriesconformalfieldsolutionsactor-criticalgorithmalgorithms
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In this paper we deploy for the first time Reinforcement-Learning algorithms in the context of the conformal-bootstrap programme to obtain numerical solutions of conformal field theories (CFTs). As an illustration, we use a soft Actor-Critic algorithm and find approximate solutions to the truncated crossing equations of two-dimensional CFTs, successfully identifying well-known theories like the 2D Ising model and the 2D CFT of a compactified scalar. Our methods can perform efficient high-dimensional searches that can be used to study arbitrary (unitary or non-unitary) CFTs in any spacetime dimension.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural Spectral Bias and Conformal Correlators I: Introduction and Applications

    hep-th 2026-04 unverdicted novelty 8.0 of 10

    Simple feed-forward neural networks trained on crossing symmetry plus a single anchor value reproduce CFT correlators to percent-level accuracy, and the authors conjecture this works because physical correlators are t...

  2. Descending into the Modular Bootstrap

    hep-th 2026-04 unverdicted novelty 7.0 of 10

    Numerical search finds candidate modular-invariant spectra with integer degeneracies for 1 < c < 8/7 and hints at a stronger gap bound near c = 1.

  3. Accurate boundary bootstrap for the three-dimensional O($N$) normal universality class

    hep-th 2025-08 conditional novelty 6.0 of 10

    High-truncation eta-minimization bootstrap yields accurate boundary critical amplitudes for the 3d O(N) normal universality class, resolving prior Monte Carlo discrepancies and giving new Ising boundary data.

  4. Machine Learning the 6d Supergravity Landscape

    hep-th 2025-05 conditional novelty 6.0 of 10

    An autoencoder and two neural classifiers, trained only on anomaly Gram matrices, provide automated clustering, outlier detection, and consistency predictions for millions of 6d supergravity building blocks.

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