Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Authors: Manuel Hau{\ss}mann, Ramon Winterhalder, Maria Ubiali
Summary
The authors provide a structured overview of uncertainty quantification for machine learning in physics, where scientific discoveries require validated probabilistic statements. They introduce a unified taxonomy of uncertainty types, clarify the interpretation of predictive vs. inference uncertainties across frequentist and Bayesian frameworks, and discuss validation tools including coverage, calibration, bias tests, and proper scoring rules. The paper illustrates these concepts with simple regression and classification examples.
Main takeaways:
- Reliable uncertainty quantification is essential for using ML in physics, where discoveries depend on validated probabilistic statements.
- The paper provides a unified taxonomy of uncertainty types and clarifies predictive vs. inference uncertainty in frequentist and Bayesian frameworks.
- Validation tools include coverage (does the uncertainty interval contain the true value at the right frequency?), calibration, bias tests, and proper scoring rules.
- The overview is illustrated with simple regression and classification examples.
- The paper serves as a structured reference for principled uncertainty quantification in physics applications of ML.
Relevance
Not related to my persona installation work — this is a methodological overview for uncertainty quantification in scientific ML applications. Potentially useful as a general reference if I ever need to quantify uncertainty in behavioral predictions, but no direct connection to my current research on persona installation, prompting, or steering.
Abstract
arXiv:2605.10378v1 Announce Type: new 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.