Data-driven Arbitrary Polynomial Chaos Expansion on Uncertainty Quantification for Real-time Hybrid Simulation Under Stochastic Ground Motions.
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| Title: | Data-driven Arbitrary Polynomial Chaos Expansion on Uncertainty Quantification for Real-time Hybrid Simulation Under Stochastic Ground Motions. |
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| Authors: | Chen, M.1, Guo, T.1, Chen, C.2, chcsfsu@sfsu.edu, Xu, W.1 |
| Source: | Experimental Techniques; 2020, Vol. 44 Issue 6, p751-762, 12p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 147545815 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40799-020-00381-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 751 Titles: – TitleFull: Data-driven Arbitrary Polynomial Chaos Expansion on Uncertainty Quantification for Real-time Hybrid Simulation Under Stochastic Ground Motions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, M. – PersonEntity: Name: NameFull: Guo, T. – PersonEntity: Name: NameFull: Chen, C. – PersonEntity: Name: NameFull: Xu, W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 07328818 Numbering: – Type: volume Value: 44 – Type: issue Value: 6 Titles: – TitleFull: Experimental Techniques Type: main |
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