No Cover Image

Consultancy Report 80 views

Utilizing Soize's Approach to Identify Parameter and Model Uncertainties

Matt Bonney Orcid Logo, Matthew Brake

Swansea University Author: Matt Bonney Orcid Logo

Full text not available from this repository: check for access using links below.

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...

Full description

Published: Office of Scientific and Technical Information (OSTI) 2014
URI: https://cronfa.swan.ac.uk/Record/cronfa65053
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2024-03-13T13:48:59Z
last_indexed 2024-03-13T13:48:59Z
id cronfa65053
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>65053</id><entry>2023-11-21</entry><title>Utilizing Soize's Approach to Identify Parameter and Model Uncertainties</title><swanseaauthors><author><sid>323110cf11dcec3e8183228a4b33e06d</sid><ORCID>0000-0002-1499-0848</ORCID><firstname>Matt</firstname><surname>Bonney</surname><name>Matt Bonney</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-11-21</date><deptcode>AERO</deptcode><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.</abstract><type>Consultancy Report</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher>Office of Scientific and Technical Information (OSTI)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Mathematics; computing</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2014</publishedYear><publishedDate>2014-10-01</publishedDate><doi>10.2172/1322274</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>AERO</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2024-03-13T13:49:02.4736352</lastEdited><Created>2023-11-21T09:39:04.4278742</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering</level></path><authors><author><firstname>Matt</firstname><surname>Bonney</surname><orcid>0000-0002-1499-0848</orcid><order>1</order></author><author><firstname>Matthew</firstname><surname>Brake</surname><order>2</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling v2 65053 2023-11-21 Utilizing Soize's Approach to Identify Parameter and Model Uncertainties 323110cf11dcec3e8183228a4b33e06d 0000-0002-1499-0848 Matt Bonney Matt Bonney true false 2023-11-21 AERO 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. Consultancy Report Office of Scientific and Technical Information (OSTI) Mathematics; computing 1 10 2014 2014-10-01 10.2172/1322274 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2024-03-13T13:49:02.4736352 2023-11-21T09:39:04.4278742 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Matt Bonney 0000-0002-1499-0848 1 Matthew Brake 2
title Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
spellingShingle Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
Matt Bonney
title_short Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
title_full Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
title_fullStr Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
title_full_unstemmed Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
title_sort Utilizing Soize's Approach to Identify Parameter and Model Uncertainties
author_id_str_mv 323110cf11dcec3e8183228a4b33e06d
author_id_fullname_str_mv 323110cf11dcec3e8183228a4b33e06d_***_Matt Bonney
author Matt Bonney
author2 Matt Bonney
Matthew Brake
format Consultancy Report
publishDate 2014
institution Swansea University
doi_str_mv 10.2172/1322274
publisher Office of Scientific and Technical Information (OSTI)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 0
active_str 0
description 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.
published_date 2014-10-01T13:48:59Z
_version_ 1793419105902002176
score 11.013082

Similar Items