UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Uncertainty quantification for machine learning: a new approach for the Critical Heat Flux application

Sep 17, 2025, 4:00 PM

30m

Amphithéâtre Claude Bloch (IPhT) (CEA Paris-Saclay)

Short-talk Deep Learning and Uncertainty Quantification Deep Learning and Uncertainty Quantification

Speaker

Michele Cazzola (CEA Paris-Saclay)

Description

Critical Heat Flux (CHF) represents a concern for the nuclear safety, as it leads to a rapid drop down in the heat transfer between a heated surface and the liquid coolant in the core of nuclear reactors. This could cause several issues to the system, including structural damage and release of radioactive material.

The main challenge related to CHF prediction is the highly non-linear relationship with the physical features it depends on. For that reason, the prediction of CHF is often affected by large uncertainties.

In this research, the CHF database provided by the U.S. Nuclear Regulatory Commission (NRC) is utilized to develop machine learning (ML) methods for CHF prediction and robust uncertainty quantification (UQ) techniques. The performance of the ML models is assessed against established data-driven strategies, while a coverage-based approach is considered for UQ methods by using conformal prediction and a quality-driven loss function. It is found that it is possible to confidently estimate the prediction uncertainties with a 95% coverage regarding experimental CHF values.

Presentation materials

abstract.pdf