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Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?

Journal Article · · Nuclear Science and Engineering
 [1];  [2];  [2];  [1];  [3];  [1]
  1. North Carolina State University, Raleigh, NC (United States)
  2. North Carolina State University, Raleigh, NC (United States); The South African Nuclear Energy Corporation SOC Ltd (Necsa), Pretoria (South Africa)
  3. U.S. Nuclear Regulatory Commission (NRC), Washington, DC (United States)
+ Show Author Affiliations
Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning, the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes have often outpaced our formal understanding of the ML algorithms. An important but under-rated area is uncertainty quantification (UQ) of ML. ML-based models are subject to approximation uncertainty when they are used to make predictions, due to sources including but not limited to, data noise, data coverage, extrapolation, imperfect model architecture and the stochastic training process. The goal of this paper is to clearly explain and illustrate the importance of UQ of ML. We will elucidate the differences in the basic concepts of UQ of physics-based models and data-driven ML models. Various sources of uncertainties in physical modeling and data-driven modeling will be discussed, demonstrated, and compared. We will also present and demonstrate a few techniques to quantify the ML prediction uncertainties, including Monte Carlo dropout, deep ensemble, Bayesian neural networks, Gaussian Processes and conformal prediction. Lastly, we will discuss the need for building a verification, validation and UQ framework to establish ML credibility.
Research Organization:
North Carolina State University, Raleigh, NC (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE)
Grant/Contract Number:
NE0009467
OSTI ID:
3000582
Journal Information:
Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering; ISSN 0029-5639; ISSN 1943-748X
Publisher:
Informa UK LimitedCopyright Statement
Country of Publication:
United States
Language:
English

References (47)

Challenges to the Reproducibility of Machine Learning Models in Health Care journal January 2020
A survey of uncertainty in deep neural networks journal July 2023
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods journal March 2021
Quantifying reactor safety margins part 1: An overview of the code scaling, applicability, and uncertainty evaluation methodology journal May 1990
Historical insights in the development of Best Estimate Plus Uncertainty safety analysis journal February 2013
Quantification of system uncertainties in activation experiments at nuclear research reactors journal December 2019
Measurement, simulation and uncertainty quantification of the neutron flux at the McMaster Nuclear Reactor journal February 2021
Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks journal August 2023
Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles journal October 2024
Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks journal September 2025
A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing journal June 2011
Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data journal February 2024
Kriging metamodeling in simulation: A review journal February 2009
Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks journal February 2025
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI journal June 2020
A review of uncertainty quantification in deep learning: Techniques, applications and challenges journal December 2021
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence journal November 2023
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons journal March 2023
The Best Estimate Plus Uncertainty (BEPU) approach in licensing of current nuclear reactors journal July 2012
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE journal August 2018
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory journal August 2018
A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes journal December 2021
A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression journal January 2015
Uncertainty quantification study of the physics-informed machine learning models for critical heat flux prediction journal May 2024
Bayesian analysis of computer code outputs: A tutorial journal October 2006
Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes journal July 2019
Surrogate modeling of advanced computer simulations using deep Gaussian processes journal March 2020
Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling journal December 2025
Aleatory or epistemic? Does it matter? journal March 2009
Verification and Validation in Scientific Computing book March 2013
Efficient Global Optimization of Expensive Black-Box Functions journal January 1998
Reproducibility standards for machine learning in the life sciences journal August 2021
Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot journal July 2021
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead journal May 2019
Advanced Methodology for Uncertainty Propagation in Computer Experiments with Large Number of Inputs journal March 2019
Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach journal February 2023
The ICSCREAM Methodology: Identification of Penalizing Configurations in Computer Experiments Using Screening and Metamodel—Applications in Thermal Hydraulics journal November 2021
Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models journal November 2022
Multi-Output Gaussian Processes for Inverse Uncertainty Quantification in Neutron Noise Analysis journal February 2023
Variational Inference: A Review for Statisticians journal July 2016
Generalizing to Unseen Domains: A Survey on Domain Generalization journal January 2022
Design and Analysis of Computer Experiments journal November 1989
Gradient-Enhanced Universal Kriging for Uncertainty Propagation journal February 2012
Conformal Prediction: A Gentle Introduction journal January 2023
Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence report February 2019
Use of Kriging Models to Approximate Deterministic Computer Models journal April 2005
Gaussian Processes for Machine Learning book January 2005

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Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
Bayesian neural network
Monte Carlo dropout
Uncertainty quantification
conformal prediction
deep ensemble