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research article

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Nemani, Venkat
•
Biggio, Luca  
•
Huan, Xun
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2023
Mechanical Systems and Signal Processing

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

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Type
research article
DOI
10.1016/j.ymssp.2023.110796
Author(s)
Nemani, Venkat
•
Biggio, Luca  
•
Huan, Xun
•
Hu, Zhen
•
Fink, Olga  
•
Tran, Anh
•
Wang, Yan
•
Xiaoge Zhang
•
Chao Hu
Date Issued

2023

Published in
Mechanical Systems and Signal Processing
Volume

205

Article Number

110796

Subjects

Machine learning

•

Uncertainty quantification

•

Engineering design

•

Prognostics and health management

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
IMOS  
Available on Infoscience
January 30, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/203309
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