Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917480542240768 |
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| author | Haußmann, Manuel Winterhalder, Ramon Ubiali, Maria |
| author_facet | Haußmann, Manuel Winterhalder, Ramon Ubiali, Maria |
| contents | Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a structured overview of uncertainty quantification in ML for physics, introducing a unified taxonomy of uncertainty and clarifying the interpretation of predictive and inference uncertainties across frequentist and Bayesian frameworks. We discuss principled validation tools, including coverage, calibration, bias tests, and proper scoring rules, and illustrate them with simple regression and classification examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10378 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation Haußmann, Manuel Winterhalder, Ramon Ubiali, Maria Machine Learning Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies High Energy Physics - Experiment High Energy Physics - Phenomenology Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a structured overview of uncertainty quantification in ML for physics, introducing a unified taxonomy of uncertainty and clarifying the interpretation of predictive and inference uncertainties across frequentist and Bayesian frameworks. We discuss principled validation tools, including coverage, calibration, bias tests, and proper scoring rules, and illustrate them with simple regression and classification examples. |
| title | Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation |
| topic | Machine Learning Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2605.10378 |