NeuMC -- a package for neural sampling for lattice field theories
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911198057857024 |
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| author | Bialas, Piotr Korcyl, Piotr Stebel, Tomasz Zapolski, Dawid |
| author_facet | Bialas, Piotr Korcyl, Piotr Stebel, Tomasz Zapolski, Dawid |
| contents | We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating some shortcomings of the Markov chain Monte-Carlo methods. Our package provides tools to create such samplers for two-dimensional field theories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuMC -- a package for neural sampling for lattice field theories Bialas, Piotr Korcyl, Piotr Stebel, Tomasz Zapolski, Dawid High Energy Physics - Lattice Machine Learning 68T07 J.2 We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating some shortcomings of the Markov chain Monte-Carlo methods. Our package provides tools to create such samplers for two-dimensional field theories. |
| title | NeuMC -- a package for neural sampling for lattice field theories |
| topic | High Energy Physics - Lattice Machine Learning 68T07 J.2 |
| url | https://arxiv.org/abs/2503.11482 |