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Generative sampling with physics-informed kernels — arXiv:2610.41864

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41864
License (as archived): CC0-1.0
Archived: 2026-11-02T08:10:23.410Z
SHA-256 of the archived text: 694993ef6a7a842c200dad3d9aa5e2bdceb77d63f36abf44725d77f9afcc27b6
Stable URL of this page: https://metraipolis.com/documento/dc_424da6b030d7

How to cite

Generative sampling with physics-informed kernels — arXiv:2610.41864 — arXiv (en), https://arxiv.org/abs/2610.41864 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_424da6b030d7 (Metraipolis archive dc_424da6b030d7, sha256 694993ef6a7a842c200dad3d9aa5e2bdceb77d63f36abf44725d77f9afcc27b6)

BibTeX

@misc{dc424da6b030d7,
  title = {Generative sampling with physics-informed kernels — arXiv:2610.41864},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41864},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_424da6b030d7; sha256 694993ef6a7a842c200dad3d9aa5e2bdceb77d63f36abf44725d77f9afcc27b6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41864. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We construct a generative network for Monte-Carlo sampling in lattice field theories and beyond, for which the layerwise propagation is computed and optimised independently on each layer. The architecture uses physics-informed renormalisation group flows that provide access to the layerwise propagation step from one layer to the next in terms of a simple first order partial differential equation for the respective renormalisation group kernel through a given layer. Thus, it transforms the generative task into that of solving once the set of independent and linear differential equations for the kernels of the transformation. As these equations are analytically known, the kernels can be refined iteratively. This allows us to structurally tackle out-of-domain problems generally encountered in generative models and opens the path to further optimisation. We illustrate the practical feasibility of the architecture within simulations in scalar field theories.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv physics.data-an, nuovi lavori del giorno).
Categorie arXiv: High Energy Physics - Lattice (hep-lat) ; Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2610.41864 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:48:00.668Z dalla pagina https://arxiv.org/list/physics.data-an/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).