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VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics — arXiv:2608.17724

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.17724
License (as archived): CC0-1.0
Archived: 2026-10-18T16:08:09.261Z
SHA-256 of the archived text: 0983e61cc0a1c70f9d7c908c60beeaf2e1732fd85eaf6f7853d39fcc0204942c
Stable URL of this page: https://metraipolis.com/documento/dc_bec2dafeb415

How to cite

VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics — arXiv:2608.17724 — arXiv (en), https://arxiv.org/abs/2608.17724 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bec2dafeb415 (Metraipolis archive dc_bec2dafeb415, sha256 0983e61cc0a1c70f9d7c908c60beeaf2e1732fd85eaf6f7853d39fcc0204942c)

BibTeX

@misc{dcbec2dafeb415,
  title = {VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics — arXiv:2608.17724},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.17724},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bec2dafeb415; sha256 0983e61cc0a1c70f9d7c908c60beeaf2e1732fd85eaf6f7853d39fcc0204942c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.17724. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantification, and robustness assessment are less systematically addressed. The VERaiPHY initiative (Validation & Evaluation for Robust AI in PHYsics) is a series of articles developed within the PHYSTAT programme, aimed at establishing statistical standards for the development, evaluation, and deployment of ML techniques. Each article focuses on a specific methodological domain from a statistics perspective and clarifies statistical questions, tests, and the interpretation of results. This opening article establishes the probabilistic, statistical, and machine learning foundations that the later contributions assume, together with the notation used throughout.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-08-19 in astro-ph.CO).
Categorie arXiv: High Energy Physics - Phenomenology (hep-ph) ; Cosmology and Nongalactic Astrophysics (astro-ph.CO); High Energy Physics - Experiment (hep-ex); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2608.17724 — 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 2026-10-18T16:07:39.196Z dalla pagina https://arxiv.org/catchup/astro-ph.CO/2026-08-19?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).