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arXiv · 2605.10378

Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation

Manuel Haußmann·Ramon Winterhalder·Maria Ubiali
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Abstract

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.

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Manuel Haußmann, Ramon Winterhalder, Maria Ubiali. 2026-05-11. Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation. https://arxiv.org/abs/2605.10378

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