A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems

Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer methods in applied mechanics and engineering 2021-11, Vol.385, p.114009, Article 114009
Hauptverfasser: Liu, Qiming, Tong, Nichen, Wu, Xingfu, Han, Xu, Chen, Chao
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 114009
container_title Computer methods in applied mechanics and engineering
container_volume 385
creator Liu, Qiming
Tong, Nichen
Wu, Xingfu
Han, Xu
Chen, Chao
description Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This paper presents a global sensitivity analysis method based on variance and covariance decomposition (VCD-GSA) of summatory functions for MIMO systems. For a MIMO system with n input variables and m output responses, a set of summatory functions can be constructed by the addition and subtraction of any two output response functions. Each output response function is represented using this set of summatory functions. The variances and covariances of all the output responses are obtained by the integral calculation of the high-dimensional model representations(HDMRs) of these summatory functions. We define the total fluctuation by the sum of the variances and covariances on multiple responses, and the partial fluctuations by the sum of partial variances of a series of summatory functions. Subsequently, we define the s-order sensitivity index of the MIMO system by the ratio of the partial fluctuation on s-order function terms in HDMRs and total fluctuation. The variable sensitivity index is the sum of all of the s-order sensitivity indices, including the contribution of the input variable. The proposed VCD-GSA method is suitable for a uniform or Gaussian distribution. It is also suitable for some complex problems involving variables with correlation. Several numerical examples and engineering applications demonstrate the advantage and practicality of the proposed VCD-GSA method. •Present a general global sensitivity analysis method (VCD-GSA) for MIMO systems.•The VCD-GSA is suitable for some problems involving dependent variables.•The evaluation of the VCD-GSA is more intuitive and comprehensive.•The VCD-GSA method can avoid the influence of a negative sensitivity index.
doi_str_mv 10.1016/j.cma.2021.114009
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2577527677</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0045782521003406</els_id><sourcerecordid>2577527677</sourcerecordid><originalsourceid>FETCH-LOGICAL-c255t-ed1575ebd2c1221513c9c773413ac24ac7857ec190d35692d345d2f49ae0bfc23</originalsourceid><addsrcrecordid>eNp9kEFr3DAQhUVpoNtNf0Bvgp690cjWyqanENKkEMglOQutNG60rK2tRl5wfkd_cGQ29BhdpCfee8x8jH0HsQEB26v9xg12I4WEDUAjRPeJraDVXSWhbj-zlRCNqnQr1Rf2lWgvymlBrti_a_4HR0z2EF7Rc8KRQg6nkGduR3uYKRAfML9Ez3eWiiOO_GRTsKPD4vDcxf_So4vDMS4FxRV7TtMw2BzTzPtpdMsv8T4mPkyHHKowHqf8_o5TXgTNlHGgS3bR2wPht_d7zZ5_3T7d3FcPj3e_b64fKieVyhV6UFrhzksHUoKC2nVO67qB2jrZWKdbpdFBJ3yttp30daO87JvOotj1TtZr9uPce0zx74SUzT5OqaxNRiqtldTbUrdmcHa5FIkS9uaYwmDTbECYBb7ZmwLfLPDNGX7J_DxnsIx_CpgMuYAFkg8JXTY-hg_Sb2k6kIY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2577527677</pqid></control><display><type>article</type><title>A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems</title><source>Elsevier ScienceDirect Journals</source><creator>Liu, Qiming ; Tong, Nichen ; Wu, Xingfu ; Han, Xu ; Chen, Chao</creator><creatorcontrib>Liu, Qiming ; Tong, Nichen ; Wu, Xingfu ; Han, Xu ; Chen, Chao</creatorcontrib><description>Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This paper presents a global sensitivity analysis method based on variance and covariance decomposition (VCD-GSA) of summatory functions for MIMO systems. For a MIMO system with n input variables and m output responses, a set of summatory functions can be constructed by the addition and subtraction of any two output response functions. Each output response function is represented using this set of summatory functions. The variances and covariances of all the output responses are obtained by the integral calculation of the high-dimensional model representations(HDMRs) of these summatory functions. We define the total fluctuation by the sum of the variances and covariances on multiple responses, and the partial fluctuations by the sum of partial variances of a series of summatory functions. Subsequently, we define the s-order sensitivity index of the MIMO system by the ratio of the partial fluctuation on s-order function terms in HDMRs and total fluctuation. The variable sensitivity index is the sum of all of the s-order sensitivity indices, including the contribution of the input variable. The proposed VCD-GSA method is suitable for a uniform or Gaussian distribution. It is also suitable for some complex problems involving variables with correlation. Several numerical examples and engineering applications demonstrate the advantage and practicality of the proposed VCD-GSA method. •Present a general global sensitivity analysis method (VCD-GSA) for MIMO systems.•The VCD-GSA is suitable for some problems involving dependent variables.•The evaluation of the VCD-GSA is more intuitive and comprehensive.•The VCD-GSA method can avoid the influence of a negative sensitivity index.</description><identifier>ISSN: 0045-7825</identifier><identifier>EISSN: 1879-2138</identifier><identifier>DOI: 10.1016/j.cma.2021.114009</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Complex variables ; Covariance ; Decomposition ; Global sensitivity analysis (GSA) ; High dimensional model representations (HDMRs) ; Multi-input multi-output system ; Normal distribution ; Response functions ; Sensitivity analysis ; Subtraction ; Summatory functions ; Variables ; Variance ; Variance and covariance decomposition (VCD)</subject><ispartof>Computer methods in applied mechanics and engineering, 2021-11, Vol.385, p.114009, Article 114009</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier BV Nov 1, 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c255t-ed1575ebd2c1221513c9c773413ac24ac7857ec190d35692d345d2f49ae0bfc23</citedby><cites>FETCH-LOGICAL-c255t-ed1575ebd2c1221513c9c773413ac24ac7857ec190d35692d345d2f49ae0bfc23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0045782521003406$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Liu, Qiming</creatorcontrib><creatorcontrib>Tong, Nichen</creatorcontrib><creatorcontrib>Wu, Xingfu</creatorcontrib><creatorcontrib>Han, Xu</creatorcontrib><creatorcontrib>Chen, Chao</creatorcontrib><title>A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems</title><title>Computer methods in applied mechanics and engineering</title><description>Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This paper presents a global sensitivity analysis method based on variance and covariance decomposition (VCD-GSA) of summatory functions for MIMO systems. For a MIMO system with n input variables and m output responses, a set of summatory functions can be constructed by the addition and subtraction of any two output response functions. Each output response function is represented using this set of summatory functions. The variances and covariances of all the output responses are obtained by the integral calculation of the high-dimensional model representations(HDMRs) of these summatory functions. We define the total fluctuation by the sum of the variances and covariances on multiple responses, and the partial fluctuations by the sum of partial variances of a series of summatory functions. Subsequently, we define the s-order sensitivity index of the MIMO system by the ratio of the partial fluctuation on s-order function terms in HDMRs and total fluctuation. The variable sensitivity index is the sum of all of the s-order sensitivity indices, including the contribution of the input variable. The proposed VCD-GSA method is suitable for a uniform or Gaussian distribution. It is also suitable for some complex problems involving variables with correlation. Several numerical examples and engineering applications demonstrate the advantage and practicality of the proposed VCD-GSA method. •Present a general global sensitivity analysis method (VCD-GSA) for MIMO systems.•The VCD-GSA is suitable for some problems involving dependent variables.•The evaluation of the VCD-GSA is more intuitive and comprehensive.•The VCD-GSA method can avoid the influence of a negative sensitivity index.</description><subject>Complex variables</subject><subject>Covariance</subject><subject>Decomposition</subject><subject>Global sensitivity analysis (GSA)</subject><subject>High dimensional model representations (HDMRs)</subject><subject>Multi-input multi-output system</subject><subject>Normal distribution</subject><subject>Response functions</subject><subject>Sensitivity analysis</subject><subject>Subtraction</subject><subject>Summatory functions</subject><subject>Variables</subject><subject>Variance</subject><subject>Variance and covariance decomposition (VCD)</subject><issn>0045-7825</issn><issn>1879-2138</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEFr3DAQhUVpoNtNf0Bvgp690cjWyqanENKkEMglOQutNG60rK2tRl5wfkd_cGQ29BhdpCfee8x8jH0HsQEB26v9xg12I4WEDUAjRPeJraDVXSWhbj-zlRCNqnQr1Rf2lWgvymlBrti_a_4HR0z2EF7Rc8KRQg6nkGduR3uYKRAfML9Ez3eWiiOO_GRTsKPD4vDcxf_So4vDMS4FxRV7TtMw2BzTzPtpdMsv8T4mPkyHHKowHqf8_o5TXgTNlHGgS3bR2wPht_d7zZ5_3T7d3FcPj3e_b64fKieVyhV6UFrhzksHUoKC2nVO67qB2jrZWKdbpdFBJ3yttp30daO87JvOotj1TtZr9uPce0zx74SUzT5OqaxNRiqtldTbUrdmcHa5FIkS9uaYwmDTbECYBb7ZmwLfLPDNGX7J_DxnsIx_CpgMuYAFkg8JXTY-hg_Sb2k6kIY</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Liu, Qiming</creator><creator>Tong, Nichen</creator><creator>Wu, Xingfu</creator><creator>Han, Xu</creator><creator>Chen, Chao</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20211101</creationdate><title>A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems</title><author>Liu, Qiming ; Tong, Nichen ; Wu, Xingfu ; Han, Xu ; Chen, Chao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c255t-ed1575ebd2c1221513c9c773413ac24ac7857ec190d35692d345d2f49ae0bfc23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Complex variables</topic><topic>Covariance</topic><topic>Decomposition</topic><topic>Global sensitivity analysis (GSA)</topic><topic>High dimensional model representations (HDMRs)</topic><topic>Multi-input multi-output system</topic><topic>Normal distribution</topic><topic>Response functions</topic><topic>Sensitivity analysis</topic><topic>Subtraction</topic><topic>Summatory functions</topic><topic>Variables</topic><topic>Variance</topic><topic>Variance and covariance decomposition (VCD)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Qiming</creatorcontrib><creatorcontrib>Tong, Nichen</creatorcontrib><creatorcontrib>Wu, Xingfu</creatorcontrib><creatorcontrib>Han, Xu</creatorcontrib><creatorcontrib>Chen, Chao</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer methods in applied mechanics and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Qiming</au><au>Tong, Nichen</au><au>Wu, Xingfu</au><au>Han, Xu</au><au>Chen, Chao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems</atitle><jtitle>Computer methods in applied mechanics and engineering</jtitle><date>2021-11-01</date><risdate>2021</risdate><volume>385</volume><spage>114009</spage><pages>114009-</pages><artnum>114009</artnum><issn>0045-7825</issn><eissn>1879-2138</eissn><abstract>Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This paper presents a global sensitivity analysis method based on variance and covariance decomposition (VCD-GSA) of summatory functions for MIMO systems. For a MIMO system with n input variables and m output responses, a set of summatory functions can be constructed by the addition and subtraction of any two output response functions. Each output response function is represented using this set of summatory functions. The variances and covariances of all the output responses are obtained by the integral calculation of the high-dimensional model representations(HDMRs) of these summatory functions. We define the total fluctuation by the sum of the variances and covariances on multiple responses, and the partial fluctuations by the sum of partial variances of a series of summatory functions. Subsequently, we define the s-order sensitivity index of the MIMO system by the ratio of the partial fluctuation on s-order function terms in HDMRs and total fluctuation. The variable sensitivity index is the sum of all of the s-order sensitivity indices, including the contribution of the input variable. The proposed VCD-GSA method is suitable for a uniform or Gaussian distribution. It is also suitable for some complex problems involving variables with correlation. Several numerical examples and engineering applications demonstrate the advantage and practicality of the proposed VCD-GSA method. •Present a general global sensitivity analysis method (VCD-GSA) for MIMO systems.•The VCD-GSA is suitable for some problems involving dependent variables.•The evaluation of the VCD-GSA is more intuitive and comprehensive.•The VCD-GSA method can avoid the influence of a negative sensitivity index.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.cma.2021.114009</doi></addata></record>
fulltext fulltext
identifier ISSN: 0045-7825
ispartof Computer methods in applied mechanics and engineering, 2021-11, Vol.385, p.114009, Article 114009
issn 0045-7825
1879-2138
language eng
recordid cdi_proquest_journals_2577527677
source Elsevier ScienceDirect Journals
subjects Complex variables
Covariance
Decomposition
Global sensitivity analysis (GSA)
High dimensional model representations (HDMRs)
Multi-input multi-output system
Normal distribution
Response functions
Sensitivity analysis
Subtraction
Summatory functions
Variables
Variance
Variance and covariance decomposition (VCD)
title A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-15T23%3A33%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20generalized%20sensitivity%20analysis%20method%20based%20on%20variance%20and%20covariance%20decomposition%20of%20summatory%20functions%20for%20multi-input%20multi-output%20systems&rft.jtitle=Computer%20methods%20in%20applied%20mechanics%20and%20engineering&rft.au=Liu,%20Qiming&rft.date=2021-11-01&rft.volume=385&rft.spage=114009&rft.pages=114009-&rft.artnum=114009&rft.issn=0045-7825&rft.eissn=1879-2138&rft_id=info:doi/10.1016/j.cma.2021.114009&rft_dat=%3Cproquest_cross%3E2577527677%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2577527677&rft_id=info:pmid/&rft_els_id=S0045782521003406&rfr_iscdi=true