Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation

Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenario...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 14; no. 6; p. 1219
Main Authors Wang, Chong, Qiang, Xin, Xu, Menghui, Wu, Tao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 13.06.2022
Subjects
Adaptive sampling
Engineering
Epistemology
Fuzzy sets
Machine learning
Mathematical functions
Mathematical models
Model accuracy
Numerical analysis
Numerical methods
Parameter identification
Probability distribution
Probability theory
Propagation
Random variables
Set theory
Simulation
Stochastic models
Uncertainty
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Summary:Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios. To this end, the mathematical models for uncertainty quantification are firstly reviewed, and theories and advances on probabilistic, non-probabilistic and hybrid ones are discussed. Subsequently, numerical methods for uncertainty propagation are broadly reviewed under different computational strategies. Thirdly, several popular single surrogate models and novel hybrid techniques are reviewed, together with some general criteria for accuracy evaluation. In addition, sample generation techniques to improve the accuracy of surrogate models are discussed for both static sampling and its adaptive version. Finally, closing remarks are provided and future prospects are suggested.
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content type line 14
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14061219