Universality of Neural Network Field Theory
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866909996494618624 |
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| author | Ferko, Christian Halverson, James Mutchler, Aaron |
| author_facet | Ferko, Christian Halverson, James Mutchler, Aaron |
| contents | We prove that any quantum field theory, or more generally any probability distribution over tempered distributions in $\mathbb{R}^d$, admits a neural network description with a countable infinity of parameters. As an example, we realize the $2d$ Liouville theory as a neural network and numerically compute the three-point function of vertex operators, finding agreement with the DOZZ formula. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Universality of Neural Network Field Theory Ferko, Christian Halverson, James Mutchler, Aaron High Energy Physics - Theory Probability We prove that any quantum field theory, or more generally any probability distribution over tempered distributions in $\mathbb{R}^d$, admits a neural network description with a countable infinity of parameters. As an example, we realize the $2d$ Liouville theory as a neural network and numerically compute the three-point function of vertex operators, finding agreement with the DOZZ formula. |
| title | Universality of Neural Network Field Theory |
| topic | High Energy Physics - Theory Probability |
| url | https://arxiv.org/abs/2601.14453 |