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Towards error bounds of the failure probability of elastic structures using reduced basis models
Summary Structural reliability methods aim at computing the probability of failure of systems with respect to prescribed limit state functions. A common practice to evaluate these limit state functions is using Monte Carlo simulations. The main drawback of this approach is the computational cost, be...
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Published in: | International journal for numerical methods in engineering 2017-11, Vol.112 (9), p.1216-1234 |
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container_title | International journal for numerical methods in engineering |
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creator | Gallimard, L. Florentin, E. Ryckelynck, D. |
description | Summary
Structural reliability methods aim at computing the probability of failure of systems with respect to prescribed limit state functions. A common practice to evaluate these limit state functions is using Monte Carlo simulations. The main drawback of this approach is the computational cost, because it requires computing a large number of deterministic finite element solutions. Surrogate models, which are built from a limited number of runs of the original model, have been developed, as substitute of the original model, to reduce the computational cost. However, these surrogate models, while decreasing drastically the computational cost, may fail in computing an accurate failure probability. In this paper, we focus on the control of the error introduced by a reduced basis surrogate model on the computation of the failure probability obtained by a Monte Carlo simulation. We propose a technique to determine bounds of this failure probability, as well as a strategy of enrichment of the reduced basis, based on limiting the bounds of the error of the failure probability for a multi‐material elastic structure. Copyright © 2017 John Wiley & Sons, Ltd. |
doi_str_mv | 10.1002/nme.5554 |
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Structural reliability methods aim at computing the probability of failure of systems with respect to prescribed limit state functions. A common practice to evaluate these limit state functions is using Monte Carlo simulations. The main drawback of this approach is the computational cost, because it requires computing a large number of deterministic finite element solutions. Surrogate models, which are built from a limited number of runs of the original model, have been developed, as substitute of the original model, to reduce the computational cost. However, these surrogate models, while decreasing drastically the computational cost, may fail in computing an accurate failure probability. In this paper, we focus on the control of the error introduced by a reduced basis surrogate model on the computation of the failure probability obtained by a Monte Carlo simulation. We propose a technique to determine bounds of this failure probability, as well as a strategy of enrichment of the reduced basis, based on limiting the bounds of the error of the failure probability for a multi‐material elastic structure. Copyright © 2017 John Wiley & Sons, Ltd.</description><identifier>ISSN: 0029-5981</identifier><identifier>EISSN: 1097-0207</identifier><identifier>DOI: 10.1002/nme.5554</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Computational efficiency ; Computer simulation ; Computing costs ; Condensed Matter ; error bounds ; Errors ; Failure ; failure probability ; finite element analysis ; Finite element method ; Materials Science ; Mathematical models ; model reduction ; Monte Carlo simulation ; Optimization algorithms ; Physics ; reduced basis ; Reliability engineering ; Structural reliability</subject><ispartof>International journal for numerical methods in engineering, 2017-11, Vol.112 (9), p.1216-1234</ispartof><rights>Copyright © 2017 John Wiley & Sons, Ltd.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3614-adfd02049ef5a7a48394c200769c0b09f70e3de226af1ba4b34f7537430255c73</citedby><cites>FETCH-LOGICAL-c3614-adfd02049ef5a7a48394c200769c0b09f70e3de226af1ba4b34f7537430255c73</cites><orcidid>0000-0003-1853-5444 ; 0000-0003-1192-6733 ; 0000-0003-3268-4892</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://minesparis-psl.hal.science/hal-01633909$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Gallimard, L.</creatorcontrib><creatorcontrib>Florentin, E.</creatorcontrib><creatorcontrib>Ryckelynck, D.</creatorcontrib><title>Towards error bounds of the failure probability of elastic structures using reduced basis models</title><title>International journal for numerical methods in engineering</title><description>Summary
Structural reliability methods aim at computing the probability of failure of systems with respect to prescribed limit state functions. A common practice to evaluate these limit state functions is using Monte Carlo simulations. The main drawback of this approach is the computational cost, because it requires computing a large number of deterministic finite element solutions. Surrogate models, which are built from a limited number of runs of the original model, have been developed, as substitute of the original model, to reduce the computational cost. However, these surrogate models, while decreasing drastically the computational cost, may fail in computing an accurate failure probability. In this paper, we focus on the control of the error introduced by a reduced basis surrogate model on the computation of the failure probability obtained by a Monte Carlo simulation. We propose a technique to determine bounds of this failure probability, as well as a strategy of enrichment of the reduced basis, based on limiting the bounds of the error of the failure probability for a multi‐material elastic structure. Copyright © 2017 John Wiley & Sons, Ltd.</description><subject>Computational efficiency</subject><subject>Computer simulation</subject><subject>Computing costs</subject><subject>Condensed Matter</subject><subject>error bounds</subject><subject>Errors</subject><subject>Failure</subject><subject>failure probability</subject><subject>finite element analysis</subject><subject>Finite element method</subject><subject>Materials Science</subject><subject>Mathematical models</subject><subject>model reduction</subject><subject>Monte Carlo simulation</subject><subject>Optimization algorithms</subject><subject>Physics</subject><subject>reduced basis</subject><subject>Reliability engineering</subject><subject>Structural reliability</subject><issn>0029-5981</issn><issn>1097-0207</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp10EFLwzAUB_AgCs4p-BECXvTQ-dI07XIcYzph6mWeY5omLqNrZtI69u1NrXjz9Mh7Px4vf4SuCUwIQHrf7PSEMZadoBEBXiSQQnGKRnHEE8an5BxdhLAFIIQBHaH3tTtIXwWsvXcel65r4sMZ3G40NtLWndd4710pS1vb9tiPdC1DaxUOre9UG0HAXbDNB_a66pSucCmDDXjnKl2HS3RmZB301W8do7eHxXq-TFavj0_z2SpRNCdZIitTxVMzrg2ThcymlGcqBShyrqAEbgrQtNJpmktDSpmVNDMFo0VGIWVMFXSM7oa9G1mLvbc76Y_CSSuWs5Xoe0BySjnwLxLtzWDjxz47HVqxdZ1v4nmC8BwI4zylUd0OSnkXgtfmby0B0WctYtaizzrSZKAHW-vjv068PC9-_Dcm-n9P</recordid><startdate>20171130</startdate><enddate>20171130</enddate><creator>Gallimard, L.</creator><creator>Florentin, E.</creator><creator>Ryckelynck, D.</creator><general>Wiley Subscription Services, Inc</general><general>Wiley</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><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-1853-5444</orcidid><orcidid>https://orcid.org/0000-0003-1192-6733</orcidid><orcidid>https://orcid.org/0000-0003-3268-4892</orcidid></search><sort><creationdate>20171130</creationdate><title>Towards error bounds of the failure probability of elastic structures using reduced basis models</title><author>Gallimard, L. ; Florentin, E. ; Ryckelynck, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3614-adfd02049ef5a7a48394c200769c0b09f70e3de226af1ba4b34f7537430255c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computational efficiency</topic><topic>Computer simulation</topic><topic>Computing costs</topic><topic>Condensed Matter</topic><topic>error bounds</topic><topic>Errors</topic><topic>Failure</topic><topic>failure probability</topic><topic>finite element analysis</topic><topic>Finite element method</topic><topic>Materials Science</topic><topic>Mathematical models</topic><topic>model reduction</topic><topic>Monte Carlo simulation</topic><topic>Optimization algorithms</topic><topic>Physics</topic><topic>reduced basis</topic><topic>Reliability engineering</topic><topic>Structural reliability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gallimard, L.</creatorcontrib><creatorcontrib>Florentin, E.</creatorcontrib><creatorcontrib>Ryckelynck, D.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & 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><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>International journal for numerical methods in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gallimard, L.</au><au>Florentin, E.</au><au>Ryckelynck, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards error bounds of the failure probability of elastic structures using reduced basis models</atitle><jtitle>International journal for numerical methods in engineering</jtitle><date>2017-11-30</date><risdate>2017</risdate><volume>112</volume><issue>9</issue><spage>1216</spage><epage>1234</epage><pages>1216-1234</pages><issn>0029-5981</issn><eissn>1097-0207</eissn><abstract>Summary
Structural reliability methods aim at computing the probability of failure of systems with respect to prescribed limit state functions. A common practice to evaluate these limit state functions is using Monte Carlo simulations. The main drawback of this approach is the computational cost, because it requires computing a large number of deterministic finite element solutions. Surrogate models, which are built from a limited number of runs of the original model, have been developed, as substitute of the original model, to reduce the computational cost. However, these surrogate models, while decreasing drastically the computational cost, may fail in computing an accurate failure probability. In this paper, we focus on the control of the error introduced by a reduced basis surrogate model on the computation of the failure probability obtained by a Monte Carlo simulation. We propose a technique to determine bounds of this failure probability, as well as a strategy of enrichment of the reduced basis, based on limiting the bounds of the error of the failure probability for a multi‐material elastic structure. Copyright © 2017 John Wiley & Sons, Ltd.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/nme.5554</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-1853-5444</orcidid><orcidid>https://orcid.org/0000-0003-1192-6733</orcidid><orcidid>https://orcid.org/0000-0003-3268-4892</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Computational efficiency Computer simulation Computing costs Condensed Matter error bounds Errors Failure failure probability finite element analysis Finite element method Materials Science Mathematical models model reduction Monte Carlo simulation Optimization algorithms Physics reduced basis Reliability engineering Structural reliability |
title | Towards error bounds of the failure probability of elastic structures using reduced basis models |
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