Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review Article Swipe

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Kaveh Abdi , Benoît Celse , Kimberley B. McAuley ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1002/cjce.25015

A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Aerospace Engineering

Physics

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Linearization Uncertainty quantification Range (aeronautics) Propagation of uncertainty Monte Carlo method Sensitivity analysis Uncertainty analysis Nonlinear system Computer science Estimation theory Mathematics Algorithm Statistics Engineering Machine learning Simulation Quantum mechanics Aerospace engineering Physics

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Type: article
Language: en
Landing Page: https://doi.org/10.1002/cjce.25015
PDF: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cjce.25015
OA Status: bronze
Cited By: 16
References: 124
Related Works: 10
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DOI: https://doi.org/10.1002/cjce.25015

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Title: Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

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Language: en

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Publication year: 2023

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Publication date: 2023-06-21

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Authors: Kaveh Abdi, Benoît Celse, Kimberley B. McAuley

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Landing page: https://doi.org/10.1002/cjce.25015

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Concepts: Linearization, Uncertainty quantification, Range (aeronautics), Propagation of uncertainty, Monte Carlo method, Sensitivity analysis, Uncertainty analysis, Nonlinear system, Computer science, Estimation theory, Mathematics, Algorithm, Statistics, Engineering, Machine learning, Simulation, Quantum mechanics, Aerospace engineering, Physics

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Cited by: 16

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