| Title: |
Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review |
| Authors: |
Abdi, Kaveh; Celse, Benoit; Mcauley, Kim |
| Contributors: |
Queen's University Kingston, Canada; IFP Energies nouvelles (IFPEN); Canadian Network for Research andInnovation in Machining Technology,Natural Sciences and EngineeringResearch Council of Canada,Grant/Award Number: RGPIN-2020-03901; EUROKIN |
| Source: |
ISSN: 0008-4034. |
| Publisher Information: |
CCSD; Wiley |
| Publication Year: |
2024 |
| Collection: |
IFP Énergies nouvelles: HAL-IFPEN |
| Subject Terms: |
Error-in-variables model; Input uncertainties; Parameter uncertainties; Prediction uncertainties; uncertainty quantification; [PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph] |
| Description: |
International audience ; 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1002/cjce.25015 |
| Availability: |
https://ifp.hal.science/hal-04501643; https://ifp.hal.science/hal-04501643v1/document; https://ifp.hal.science/hal-04501643v1/file/Can%20J%20Chem%20Eng%20-%202023%20-%20Abdi%20-%20Propagating%20input%20uncertainties%20into%20parameter%20uncertainties%20and%20model%20prediction.pdf; https://doi.org/10.1002/cjce.25015 |
| Rights: |
http://creativecommons.org/licenses/by-nc-nd/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.5ED9FCD8 |
| Database: |
BASE |