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- Publisher Website: 10.1016/j.advengsoft.2019.102687
- Scopus: eid_2-s2.0-85068269545
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Article: An efficient surrogate-aided importance sampling framework for reliability analysis
Title | An efficient surrogate-aided importance sampling framework for reliability analysis |
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Authors | |
Keywords | Stochastic sampling Metamodel Importance sampling Reliability analysis Active learning Design of experiment |
Issue Date | 2019 |
Citation | Advances in Engineering Software, 2019, v. 135, article no. 102687 How to Cite? |
Abstract | © 2019 Elsevier Ltd Surrogates in lieu of expensive-to-evaluate performance functions can accelerate the reliability analysis greatly. This paper proposes a new two-stage framework for surrogate-aided reliability analysis named Surrogates for Importance Sampling (S4IS). In the first stage, a coarse surrogate is built to gain the information about failure regions. The second stage zooms into the important regions and improves the accuracy of the failure probability estimator by adaptively selecting support points. The learning functions are proposed to guide the selection of support points such that the exploration and exploitation can be dynamically balanced. As a generic framework, S4IS has the potential to incorporate different types of surrogates (Gaussian Processes, Support Vector Machines, Neural Network, etc.). The effectiveness and efficiency of S4IS are validated by five illustrative examples, which involve system reliability, highly nonlinear limit-state functions, small failure probability and moderately high dimensionality. The implementation of S4IS is made available to download at https://sites.google.com/site/josephsaihungcheung/. |
Persistent Identifier | http://hdl.handle.net/10722/296191 |
ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 0.826 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Wang Sheng | - |
dc.contributor.author | Cheung, Sai Hung | - |
dc.contributor.author | Cao, Wen Jun | - |
dc.date.accessioned | 2021-02-11T04:53:02Z | - |
dc.date.available | 2021-02-11T04:53:02Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Advances in Engineering Software, 2019, v. 135, article no. 102687 | - |
dc.identifier.issn | 0965-9978 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296191 | - |
dc.description.abstract | © 2019 Elsevier Ltd Surrogates in lieu of expensive-to-evaluate performance functions can accelerate the reliability analysis greatly. This paper proposes a new two-stage framework for surrogate-aided reliability analysis named Surrogates for Importance Sampling (S4IS). In the first stage, a coarse surrogate is built to gain the information about failure regions. The second stage zooms into the important regions and improves the accuracy of the failure probability estimator by adaptively selecting support points. The learning functions are proposed to guide the selection of support points such that the exploration and exploitation can be dynamically balanced. As a generic framework, S4IS has the potential to incorporate different types of surrogates (Gaussian Processes, Support Vector Machines, Neural Network, etc.). The effectiveness and efficiency of S4IS are validated by five illustrative examples, which involve system reliability, highly nonlinear limit-state functions, small failure probability and moderately high dimensionality. The implementation of S4IS is made available to download at https://sites.google.com/site/josephsaihungcheung/. | - |
dc.language | eng | - |
dc.relation.ispartof | Advances in Engineering Software | - |
dc.subject | Stochastic sampling | - |
dc.subject | Metamodel | - |
dc.subject | Importance sampling | - |
dc.subject | Reliability analysis | - |
dc.subject | Active learning | - |
dc.subject | Design of experiment | - |
dc.title | An efficient surrogate-aided importance sampling framework for reliability analysis | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.advengsoft.2019.102687 | - |
dc.identifier.scopus | eid_2-s2.0-85068269545 | - |
dc.identifier.volume | 135 | - |
dc.identifier.spage | article no. 102687 | - |
dc.identifier.epage | article no. 102687 | - |
dc.identifier.eissn | 1873-5339 | - |
dc.identifier.isi | WOS:000502030600003 | - |
dc.identifier.issnl | 0965-9978 | - |