Consultancy Report 215 views
Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
Matt Bonney 
,
Matthew Brake
,
Matthew Brake
Swansea University Author:
Matt Bonney
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DOI (Published version): 10.2172/1322274
Abstract
Quantifying uncertainty in model parameters is a challenging task for analysts. Soize has derived a method that is able to characterize both model and parameter uncertainty independently. This method is explained with the assumption that some experimental data is available, and is divided into seven...
| Published: |
Office of Scientific and Technical Information (OSTI)
2014
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65053 |
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| Abstract: |
Quantifying uncertainty in model parameters is a challenging task for analysts. Soize has derived a method that is able to characterize both model and parameter uncertainty independently. This method is explained with the assumption that some experimental data is available, and is divided into seven steps. Monte Carlo analyses are performed to select the optimal dispersion variable to match the experimental data. Along with the nominal approach, an alternative distribution can be used along with corrections that can be utilized to expand the scope of this method. This method is one of a very few methods that can quantify uncertainty in the model form independently of the input parameters. Two examples are provided to illustrate the methodology, and example code is provided in the Appendix. |
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| Keywords: |
Mathematics; computing |
| College: |
Faculty of Science and Engineering |