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[2602.21701] Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux
Summary
This article explores the application of coverage-oriented uncertainty quantification (UQ) in scientific machine learning, focusing on the Critical Heat Flux (CHF) to improve model predictions across complex physical regimes.
Why It Matters
Understanding how to effectively quantify uncertainty in machine learning models is crucial for accurately representing physical systems, especially those with multi-regime behaviors. This research provides insights into methodologies that enhance predictive accuracy and reliability in scientific applications, which is vital for fields like engineering and physics.
Key Takeaways
- Coverage-oriented UQ can reshape model representation to better match complex physical regimes.
- Post-hoc methods ensure statistical calibration but may not adapt to intrinsic data variability.
- End-to-end pipelines, including Bayesian approaches, enhance both prediction accuracy and uncertainty estimation.
- The study emphasizes the importance of UQ as an integral part of the learning process rather than a final assessment.
- The Critical Heat Flux case study illustrates the challenges and solutions in modeling non-linear physical systems.
Computer Science > Machine Learning arXiv:2602.21701 (cs) [Submitted on 25 Feb 2026] Title:Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux Authors:Michele Cazzola, Alberto Ghione, Lucia Sargentini, Julien Nespoulous, Riccardo Finotello View a PDF of the paper titled Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux, by Michele Cazzola and 4 other authors View PDF HTML (experimental) Abstract:A central challenge in scientific machine learning (ML) is the correct representation of physical systems governed by multi-regime behaviours. In these scenarios, standard data analysis techniques often fail to capture the nature of the data, as the system's response varies significantly across the state space due to its stochasticity and the different physical regimes. Uncertainty quantification (UQ) should thus not be viewed merely as a safety assessment, but as a support to the learning task itself, guiding the model to internalise the behaviour of the data. We address this by focusing on the Critical Heat Flux (CHF) benchmark and dataset presented by the OECD/NEA Expert Group on Reactor Systems Multi-Physics. This case study represents a test for scientific ML due to the non-linear dependence of CHF on the inputs and the existence of distinct microscopic physical regimes. These regimes exhibit diverse statistical profiles, a complex...
