A sequential linear programming (SLP) approach for uncertainty analysis-based data-driven computational mechanics
In this article, an efficient sequential linear programming algorithm (SLP) for uncertainty analysis-based data-driven computational mechanics (UA-DDCM) is presented. By assuming that the uncertain constitutive relationship embedded behind the prescribed data set can be characterized through a conve...
Saved in:
| Published in | Computational mechanics Vol. 73; no. 4; pp. 943 - 965 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2024
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0178-7675 1432-0924 |
| DOI | 10.1007/s00466-023-02395-8 |
Cover
| Abstract | In this article, an efficient sequential linear programming algorithm (SLP) for uncertainty analysis-based data-driven computational mechanics (UA-DDCM) is presented. By assuming that the uncertain constitutive relationship embedded behind the prescribed data set can be characterized through a convex combination of the local data points, the upper and lower bounds of structural responses pertaining to the given data set, which are more valuable for making decisions in engineering design, can be found by solving a sequential of linear programming problems very efficiently. Numerical examples demonstrate the effectiveness of the proposed approach on sparse data set and its robustness with respect to the existence of noise and outliers in the data set. |
|---|---|
| AbstractList | In this article, an efficient sequential linear programming algorithm (SLP) for uncertainty analysis-based data-driven computational mechanics (UA-DDCM) is presented. By assuming that the uncertain constitutive relationship embedded behind the prescribed data set can be characterized through a convex combination of the local data points, the upper and lower bounds of structural responses pertaining to the given data set, which are more valuable for making decisions in engineering design, can be found by solving a sequential of linear programming problems very efficiently. Numerical examples demonstrate the effectiveness of the proposed approach on sparse data set and its robustness with respect to the existence of noise and outliers in the data set. |
| Audience | Academic |
| Author | Guo, Xu Du, Zongliang Huang, Mengcheng Tang, Shan Liu, Chang |
| Author_xml | – sequence: 1 givenname: Mengcheng surname: Huang fullname: Huang, Mengcheng organization: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology – sequence: 2 givenname: Chang surname: Liu fullname: Liu, Chang organization: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Ningbo Institute of Dalian University of Technology – sequence: 3 givenname: Zongliang surname: Du fullname: Du, Zongliang email: zldu@dlut.edu.cn organization: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Ningbo Institute of Dalian University of Technology – sequence: 4 givenname: Shan surname: Tang fullname: Tang, Shan organization: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Ningbo Institute of Dalian University of Technology – sequence: 5 givenname: Xu surname: Guo fullname: Guo, Xu email: guoxu@dlut.edu.cn organization: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Ningbo Institute of Dalian University of Technology |
| BookMark | eNp9kU-LHCEQxSVsILObfIGchFySg5tS2249Dkv-wUACSc5So_asS7c9q05gvn2cdCCQwyIiFO9n1at3Ta7SkgIhrznccoDhfQHo-p6BkJdrFNPPyIZ3UjAworsiG-CDZkM_qBfkupQHAK60VBvyuKUlPJ5CqhEnOsUUMNNjXg4Z5zmmA337ffftHcVjq6G7p-OS6Sm5kCvGVM8UE07nEgvbYwmeeqzIfI6_QqJumY-nijUuTUPn4O4xRVdekucjTiW8-vvekJ8fP_y4-8x2Xz99udvumOtEV5kK3jgF_d5w1e971fHRdci5HPyogEvOUSvtldkPIkjtuVEoJOjRgQ9yFPKGvFn_bZM3g6Xah-WU2yjFSpDSiMGAbqrbVXXAKdiYxqVmdO34MEfXljzGVt8O2oi-VwoaoFfA5aWUHEbr4mqygXGyHOwlEbsmYlsa9k8i9tJL_Icec5wxn5-G5AqVJk6HkP_ZeIL6DQ1ioJ4 |
| CitedBy_id | crossref_primary_10_1007_s00158_024_03949_x crossref_primary_10_1299_jamdsm_2024jamdsm0064 crossref_primary_10_1007_s13160_024_00657_3 |
| Cites_doi | 10.1007/s00466-019-01731-1 10.1002/nme.5721 10.1016/j.compstruc.2017.07.031 10.1016/j.cma.2017.11.013 10.1137/S1052623400369235 10.1007/s10589-007-9096-y 10.1016/j.cma.2020.112955 10.1016/j.cma.2020.113484 10.1002/nme.5913 10.1016/j.cma.2020.113499 10.1007/s11590-019-01409-w 10.1016/j.cma.2021.114034 10.1016/j.cma.2016.02.001 10.1002/nme.5716 10.1016/j.cma.2021.113855 10.1007/s00205-020-01490-x 10.1007/s00466-019-01725-z 10.1016/j.cma.2017.07.039 10.1007/s13160-018-0323-y 10.1016/j.cma.2019.112791 10.1016/j.cma.2020.112898 10.1007/s13160-020-00423-1 10.1007/s00466-017-1440-1 10.1007/s11831-016-9197-9 10.1007/s00205-017-1214-0 10.1002/zamm.202100482 10.1016/j.cma.2019.112587 10.1115/1.4051594 10.1016/j.cma.2021.113756 10.1016/j.cma.2020.113390 10.1016/j.mechmat.2019.103087 10.1016/j.cma.2019.02.016 10.1002/nme.6389 10.1017/dce.2020.20 10.1017/CBO9780511804441 10.1080/00401706.1962.10490033 10.1007/s10107-009-0290-9 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. COPYRIGHT 2024 Springer |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: COPYRIGHT 2024 Springer |
| DBID | AAYXX CITATION 8FE 8FG ABJCF AFKRA BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS |
| DOI | 10.1007/s00466-023-02395-8 |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central ProQuest Central Technology collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection |
| DatabaseTitle | CrossRef Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic ProQuest Central (New) Engineering Collection ProQuest One Academic (New) |
| DatabaseTitleList | Engineering Database |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering |
| EISSN | 1432-0924 |
| EndPage | 965 |
| ExternalDocumentID | A789266550 10_1007_s00466_023_02395_8 |
| GrantInformation_xml | – fundername: Higher Education Discipline Innovation Project grantid: B14013 funderid: http://dx.doi.org/10.13039/501100013314 – fundername: National Key Research and Development Program of China grantid: 2020YFB1709400 funderid: http://dx.doi.org/10.13039/501100012166 – fundername: National Natural Science Foundation of China grantid: 11821202; 12002073; 12002077 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | -5B -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 1N0 1SB 203 28- 29F 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFSI ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCEE ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 E.L EAD EAP EBLON EBS EIOEI EJD EMK EPL ESBYG ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IHE IJ- IKXTQ ISR ITC ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW L6V LAS LLZTM M4Y M7S MA- MK~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P9P PF0 PT4 PT5 PTHSS QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SCV SDH SDM SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW VH1 W23 W48 WIP WK8 XH6 YLTOR Z45 Z5O Z7R Z7S Z7V Z7X Z7Y Z7Z Z83 Z86 Z88 Z8M Z8N Z8P Z8R Z8S Z8T Z8W Z92 ZMTXR _50 ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PUEGO DWQXO PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c424t-5ed9c506b9156b6541fc4a1137df501311a858d59b72e38d195a2308fc0de3f23 |
| IEDL.DBID | BENPR |
| ISSN | 0178-7675 |
| IngestDate | Tue Aug 12 18:13:03 EDT 2025 Mon Oct 20 16:51:19 EDT 2025 Thu Apr 24 23:03:16 EDT 2025 Wed Oct 01 04:31:35 EDT 2025 Fri Feb 21 02:40:58 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Linear programming Data-driven computational mechanics Uncertainty analysis Interior-point method |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c424t-5ed9c506b9156b6541fc4a1137df501311a858d59b72e38d195a2308fc0de3f23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3033927908 |
| PQPubID | 2043755 |
| PageCount | 23 |
| ParticipantIDs | proquest_journals_3033927908 gale_infotracacademiconefile_A789266550 crossref_citationtrail_10_1007_s00466_023_02395_8 crossref_primary_10_1007_s00466_023_02395_8 springer_journals_10_1007_s00466_023_02395_8 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-01 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationSubtitle | Solids, Materials, Complex Fluids, Fluid-Structure-Interaction, Biological Systems, Micromechanics, Multiscale Mechanics, Additive Manufacturing |
| PublicationTitle | Computational mechanics |
| PublicationTitleAbbrev | Comput Mech |
| PublicationYear | 2024 |
| Publisher | Springer Berlin Heidelberg Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer – name: Springer Nature B.V |
| References | He, Chen (CR3) 2020; 363 Conti, Müller, Ortiz (CR7) 2020; 237 Eggersmann, Kirchdoerfer, Reese, Stainier, Ortiz (CR14) 2019; 350 Kanno (CR4) 2018; 35 Carrara, De Lorenzis, Stainier, Ortiz (CR12) 2020; 372 Kirchdoerfer, Ortiz (CR2) 2017; 326 Kanno (CR31) 2023; 103 CR39 Yildirim, Wright (CR36) 2002; 12 CR37 CR13 CR35 CR34 Platzer, Leygue, Stainier, Ortiz (CR10) 2021; 379 Dalémat, Coret, Leygue, Verron (CR18) 2019; 136 Kirchdoerfer, Ortiz (CR8) 2018; 113 CR32 Du, Du, Wei, Zhang, Guo (CR33) 2018; 116 Leygue, Coret, Réthoré, Stainier, Verron (CR15) 2018; 331 Tang, Li, Qiu, Yang, Saha, Mojumder, Liu, Guo (CR27) 2020; 364 Conti, Müller, Ortiz (CR6) 2018; 229 Nguyen, Rambausek, Keip (CR11) 2020; 365 John, Yıldırım (CR38) 2008; 41 Gebhardt, Steinbach, Schillinger, Rolfes (CR23) 2020; 121 Eggersmann, Stainier, Ortiz, Reese (CR24) 2021; 373 Tang, Zhang, Yang, Li, Liu, Guo (CR28) 2019; 357 Réthoré, Leygue, Coret, Stainier, Verron (CR17) 2018; 113 Ibañez, Abisset-Chavanne, Aguado, Gonzalez, Cueto, Chinesta (CR20) 2016; 25 He, He, Chen (CR25) 2021; 385 Eggersmann, Stainier, Ortiz, Reese (CR26) 2021; 382 Kirchdoerfer, Ortiz (CR1) 2016; 304 Tang, Yang, Qiu, Fleming, Liu, Guo (CR29) 2021; 373 Kanno (CR22) 2020; 38 Nguyen, Keip (CR9) 2018; 194 Stainier, Leygue, Ortiz (CR16) 2019; 64 Leygue, Seghir, Réthoré, Coret, Verron, Stainier (CR19) 2019; 64 Ibañez, Borzacchiello, Aguado, Abisset-Chavanne, Cueto, Ladeveze, Chinesta (CR21) 2017; 60 Guo, Du, Liu, Tang (CR30) 2021; 88 Kanno (CR5) 2019; 13 Y Kanno (2395_CR31) 2023; 103 S Tang (2395_CR29) 2021; 373 LTK Nguyen (2395_CR9) 2018; 194 T Kirchdoerfer (2395_CR1) 2016; 304 QZ He (2395_CR3) 2020; 363 LTK Nguyen (2395_CR11) 2020; 365 J Réthoré (2395_CR17) 2018; 113 XL He (2395_CR25) 2021; 385 Y Kanno (2395_CR22) 2020; 38 S Conti (2395_CR7) 2020; 237 P Carrara (2395_CR12) 2020; 372 R Eggersmann (2395_CR24) 2021; 373 Y Kanno (2395_CR5) 2019; 13 E John (2395_CR38) 2008; 41 R Eggersmann (2395_CR26) 2021; 382 R Ibañez (2395_CR20) 2016; 25 R Eggersmann (2395_CR14) 2019; 350 L Stainier (2395_CR16) 2019; 64 S Conti (2395_CR6) 2018; 229 A Platzer (2395_CR10) 2021; 379 S Tang (2395_CR27) 2020; 364 S Tang (2395_CR28) 2019; 357 Y Kanno (2395_CR4) 2018; 35 M Dalémat (2395_CR18) 2019; 136 X Guo (2395_CR30) 2021; 88 R Ibañez (2395_CR21) 2017; 60 T Kirchdoerfer (2395_CR2) 2017; 326 JM Du (2395_CR33) 2018; 116 A Leygue (2395_CR19) 2019; 64 EA Yildirim (2395_CR36) 2002; 12 2395_CR32 CG Gebhardt (2395_CR23) 2020; 121 2395_CR34 2395_CR13 2395_CR35 2395_CR37 T Kirchdoerfer (2395_CR8) 2018; 113 A Leygue (2395_CR15) 2018; 331 2395_CR39 |
| References_xml | – volume: 64 start-page: 381 year: 2019 end-page: 393 ident: CR16 article-title: Model-free data-driven methods in mechanics: material data identification and solvers publication-title: Comput Mech doi: 10.1007/s00466-019-01731-1 – volume: 113 start-page: 1810 year: 2018 end-page: 1826 ident: CR17 article-title: Computational measurements of stress fields from digital images publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5721 – volume: 194 start-page: 97 year: 2018 end-page: 115 ident: CR9 article-title: A data-driven approach to nonlinear elasticity publication-title: Comput Struct doi: 10.1016/j.compstruc.2017.07.031 – volume: 331 start-page: 184 year: 2018 end-page: 196 ident: CR15 article-title: Data-based derivation of material response publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2017.11.013 – ident: CR39 – volume: 12 start-page: 782 year: 2002 end-page: 810 ident: CR36 article-title: Warm-start strategies in interior-point methods for linear programming publication-title: SIAM J Optim doi: 10.1137/S1052623400369235 – ident: CR37 – volume: 41 start-page: 151 year: 2008 end-page: 183 ident: CR38 article-title: Implementation of warm-start strategies in interior-point methods for linear programming in fixed dimension publication-title: Comput Optim Appl doi: 10.1007/s10589-007-9096-y – volume: 364 year: 2020 ident: CR27 article-title: MAP123-EP: A mechanistic-based data-driven approach for numerical elastoplastic analysis publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.112955 – volume: 373 year: 2021 ident: CR29 article-title: MAP123-EPF: A mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113484 – volume: 116 start-page: 21 year: 2018 end-page: 42 ident: CR33 article-title: Exact response bound analysis of truss structures via linear mixed 0–1 programming and sensitivity bounding technique publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5913 – volume: 373 year: 2021 ident: CR24 article-title: Model-free data-driven computational mechanics enhanced by tensor voting publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113499 – volume: 13 start-page: 1505 year: 2019 end-page: 1514 ident: CR5 article-title: Mixed-integer programming formulation of a data-driven solver in computational elasticity publication-title: Optim Lett doi: 10.1007/s11590-019-01409-w – volume: 385 year: 2021 ident: CR25 article-title: Deep autoencoders for physics-constrained data-driven nonlinear materials modeling publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.114034 – ident: CR35 – volume: 304 start-page: 81 year: 2016 end-page: 101 ident: CR1 article-title: Data-driven computational mechanics publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2016.02.001 – volume: 113 start-page: 1697 year: 2018 end-page: 1710 ident: CR8 article-title: Data-driven computing in dynamics publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5716 – volume: 382 year: 2021 ident: CR26 article-title: Efficient data structures for model-free data-driven computational mechanics publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.113855 – volume: 237 start-page: 1 year: 2020 end-page: 33 ident: CR7 article-title: Data-driven finite elasticity publication-title: Arch Ration Mech Anal doi: 10.1007/s00205-020-01490-x – volume: 64 start-page: 501 year: 2019 end-page: 509 ident: CR19 article-title: Non-parametric material state field extraction from full field measurements publication-title: Comput Mech doi: 10.1007/s00466-019-01725-z – volume: 326 start-page: 622 year: 2017 end-page: 641 ident: CR2 article-title: Data driven computing with noisy material data sets publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2017.07.039 – volume: 35 start-page: 1085 year: 2018 end-page: 1101 ident: CR4 article-title: Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach publication-title: Jpn J Ind Appl Math doi: 10.1007/s13160-018-0323-y – volume: 363 year: 2020 ident: CR3 article-title: A physics-constrained data-driven approach based on locally convex reconstruction for noisy database publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.112791 – volume: 365 year: 2020 ident: CR11 article-title: Variational framework for distance-minimizing method in data-driven computational mechanics publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.112898 – volume: 38 start-page: 39 year: 2020 end-page: 77 ident: CR22 article-title: A kernel method for learning constitutive relation in data-driven computational elasticity publication-title: Jpn J Ind Appl Math doi: 10.1007/s13160-020-00423-1 – volume: 60 start-page: 813 year: 2017 end-page: 826 ident: CR21 article-title: Data-driven non-linear elasticity: constitutive manifold construction and problem discretization publication-title: Comput Mech doi: 10.1007/s00466-017-1440-1 – volume: 25 start-page: 47 year: 2016 end-page: 57 ident: CR20 article-title: A manifold learning approach to data-driven computational elasticity and inelasticity publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-016-9197-9 – volume: 229 start-page: 79 year: 2018 end-page: 123 ident: CR6 article-title: Data-driven problems in elasticity publication-title: Arch Ration Mech Anal doi: 10.1007/s00205-017-1214-0 – ident: CR13 – volume: 103 start-page: 18 year: 2023 ident: CR31 article-title: Computation-with-confidence for static elasticity: Data-driven approach with order statistics publication-title: ZAMM - J Appl Math Mech doi: 10.1002/zamm.202100482 – ident: CR32 – ident: CR34 – volume: 357 year: 2019 ident: CR28 article-title: MAP123: A data-driven approach to use 1D data for 3D nonlinear elastic materials modeling publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.112587 – volume: 88 year: 2021 ident: CR30 article-title: A new uncertainty analysis-based framework for data-driven computational mechanics publication-title: J Appl Mech-T Asme doi: 10.1115/1.4051594 – volume: 379 year: 2021 ident: CR10 article-title: Finite element solver for data-driven finite strain elasticity publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.113756 – volume: 372 year: 2020 ident: CR12 article-title: Data-driven fracture mechanics publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113390 – volume: 136 year: 2019 ident: CR18 article-title: Measuring stress field without constitutive equation publication-title: Mech Mater doi: 10.1016/j.mechmat.2019.103087 – volume: 350 start-page: 81 year: 2019 end-page: 99 ident: CR14 article-title: Model-free data-driven inelasticity publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.02.016 – volume: 121 start-page: 5447 year: 2020 end-page: 5468 ident: CR23 article-title: A framework for data-driven structural analysis in general elasticity based on nonlinear optimization: the dynamic case publication-title: Int J Numer Methods Eng doi: 10.1002/nme.6389 – volume: 326 start-page: 622 year: 2017 ident: 2395_CR2 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2017.07.039 – volume: 357 year: 2019 ident: 2395_CR28 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.112587 – ident: 2395_CR13 doi: 10.1017/dce.2020.20 – volume: 331 start-page: 184 year: 2018 ident: 2395_CR15 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2017.11.013 – volume: 12 start-page: 782 year: 2002 ident: 2395_CR36 publication-title: SIAM J Optim doi: 10.1137/S1052623400369235 – volume: 64 start-page: 381 year: 2019 ident: 2395_CR16 publication-title: Comput Mech doi: 10.1007/s00466-019-01731-1 – volume: 373 year: 2021 ident: 2395_CR24 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113499 – volume: 237 start-page: 1 year: 2020 ident: 2395_CR7 publication-title: Arch Ration Mech Anal doi: 10.1007/s00205-020-01490-x – volume: 194 start-page: 97 year: 2018 ident: 2395_CR9 publication-title: Comput Struct doi: 10.1016/j.compstruc.2017.07.031 – volume: 13 start-page: 1505 year: 2019 ident: 2395_CR5 publication-title: Optim Lett doi: 10.1007/s11590-019-01409-w – volume: 116 start-page: 21 year: 2018 ident: 2395_CR33 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5913 – volume: 385 year: 2021 ident: 2395_CR25 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.114034 – volume: 365 year: 2020 ident: 2395_CR11 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.112898 – ident: 2395_CR35 – ident: 2395_CR32 doi: 10.1017/CBO9780511804441 – volume: 304 start-page: 81 year: 2016 ident: 2395_CR1 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2016.02.001 – volume: 136 year: 2019 ident: 2395_CR18 publication-title: Mech Mater doi: 10.1016/j.mechmat.2019.103087 – volume: 379 year: 2021 ident: 2395_CR10 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.113756 – volume: 350 start-page: 81 year: 2019 ident: 2395_CR14 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.02.016 – volume: 363 year: 2020 ident: 2395_CR3 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2019.112791 – volume: 60 start-page: 813 year: 2017 ident: 2395_CR21 publication-title: Comput Mech doi: 10.1007/s00466-017-1440-1 – volume: 113 start-page: 1697 year: 2018 ident: 2395_CR8 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5716 – volume: 64 start-page: 501 year: 2019 ident: 2395_CR19 publication-title: Comput Mech doi: 10.1007/s00466-019-01725-z – volume: 88 year: 2021 ident: 2395_CR30 publication-title: J Appl Mech-T Asme doi: 10.1115/1.4051594 – volume: 103 start-page: 18 year: 2023 ident: 2395_CR31 publication-title: ZAMM - J Appl Math Mech doi: 10.1002/zamm.202100482 – volume: 25 start-page: 47 year: 2016 ident: 2395_CR20 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-016-9197-9 – volume: 364 year: 2020 ident: 2395_CR27 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.112955 – volume: 113 start-page: 1810 year: 2018 ident: 2395_CR17 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.5721 – ident: 2395_CR39 doi: 10.1080/00401706.1962.10490033 – volume: 38 start-page: 39 year: 2020 ident: 2395_CR22 publication-title: Jpn J Ind Appl Math doi: 10.1007/s13160-020-00423-1 – ident: 2395_CR37 doi: 10.1007/s10107-009-0290-9 – volume: 373 year: 2021 ident: 2395_CR29 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113484 – volume: 372 year: 2020 ident: 2395_CR12 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113390 – ident: 2395_CR34 – volume: 382 year: 2021 ident: 2395_CR26 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2021.113855 – volume: 41 start-page: 151 year: 2008 ident: 2395_CR38 publication-title: Comput Optim Appl doi: 10.1007/s10589-007-9096-y – volume: 229 start-page: 79 year: 2018 ident: 2395_CR6 publication-title: Arch Ration Mech Anal doi: 10.1007/s00205-017-1214-0 – volume: 121 start-page: 5447 year: 2020 ident: 2395_CR23 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.6389 – volume: 35 start-page: 1085 year: 2018 ident: 2395_CR4 publication-title: Jpn J Ind Appl Math doi: 10.1007/s13160-018-0323-y |
| SSID | ssj0015835 |
| Score | 2.4256165 |
| Snippet | In this article, an efficient sequential linear programming algorithm (SLP) for uncertainty analysis-based data-driven computational mechanics (UA-DDCM) is... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 943 |
| SubjectTerms | Algorithms Classical and Continuum Physics Computational mechanics Computational Science and Engineering Constitutive relationships Data analysis Data points Datasets Design engineering Engineering Linear programming Lower bounds Mechanics Original Paper Outliers (statistics) Robustness (mathematics) Structural response Theoretical and Applied Mechanics Uncertainty analysis |
| SummonAdditionalLinks | – databaseName: SpringerLink Journals (ICM) dbid: U2A link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA86X_TBj6k4nZIHQUUDbZM06WMRxxAVQQd7C22SgrBNXeeD_72XNp3fgs9Nr6WXy_2ud_c7hA5zE2nBGSVG8pgwoQVJoiwnzBgG_i-LbcWzfX0T9wfscsiHvimsbKrdm5RkdVLPm91cKOcKZl3ekSacyEW0xB2dF-ziQZTOcwdc1mM1Q4iPHFWJb5X5WcYnd_T1UP6WHa2cTm8drXq0iNNavRtowU7aaM0jR-ztsmyjlQ-0gpvoOcV1hTRY7wg7HJlNsS_EGsMKfHx3dXuCGzpxDLgVg3uriwNmrzjzRCXEuTiDXREpMVN3LGJdDYHwPxDx2Lq-4QddbqFB7-L-vE_8aAWiWcRmhFuTaB7EeQLxW-5mgReaZWFIhSl4RcGTSS4NT3IRWSpNmPAMghVZ6MBYWkR0G7UmjxO7g3AYah0UgANDWjAtAGIwI0xsASrl1BjaQWHzhZX2vONu_MVIzRmTK60o0IiqtKJkB53O73mqWTf-XH3kFKecSYJknfnOAng_R26lUiETwCEQi3VQt9Gt8rZaKnDiABJFEoCgs0bf75d_f-7u_5bvoeUIEFFd9tNFrdn0xe4DopnlB9UGfgO1Lerx priority: 102 providerName: Springer Nature |
| Title | A sequential linear programming (SLP) approach for uncertainty analysis-based data-driven computational mechanics |
| URI | https://link.springer.com/article/10.1007/s00466-023-02395-8 https://www.proquest.com/docview/3033927908 |
| Volume | 73 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1432-0924 dateEnd: 20241103 omitProxy: true ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: ABDBF dateStart: 20030401 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: EBSCOhost Mathematics Source - HOST customDbUrl: eissn: 1432-0924 dateEnd: 20241103 omitProxy: false ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: AMVHM dateStart: 19860301 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/mathematics-source providerName: EBSCOhost – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1432-0924 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: AFBBN dateStart: 19860301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: Proquest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1432-0924 dateEnd: 20241103 omitProxy: true ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: BENPR dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1432-0924 dateEnd: 20241103 omitProxy: true ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: 8FG dateStart: 20190101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1432-0924 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1432-0924 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015835 issn: 0178-7675 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3db9MwED9t7Qs88DFAK4zKD0iAwKKJ7dh5QCigdhMf1QRUGk9WYjsS0tZtbXngv-fOcTo-xF4Tx4ly9t3vfHe_A3jS-NxpJQX3RhVcaqd5mdcNl95LtH91ESLP9qd5cbSQ70_UyQ7M-1oYSqvsdWJU1P7c0Rn5K1S1aMp1OTFvLi45dY2i6GrfQqNOrRX860gxtgvDnJixBjB8O50ff97GFZTpWm5m6DsRjUkqo4nFdOQqUkIuxTVFqbj5w1T9rbD_iZxGgzS7A7cSkmRVJ_q7sBOWe3A7oUqW9ux6D27-Rjl4Dy4r1mVP484-ZYQx6xVLSVpnOII9-_Lx-DnrqcYZYlqGpq9LHNj8ZHUiMeFk_jyjBFPuV6QymYsNItLhIjsLVFP83a3vw2I2_fruiKe2C9zJXG64Cr50alI0Jfp2DfUJb52ss0xo36pIz1MbZbwqG50HYXxWqhodGdO6iQ-izcUDGCzPl2EfWJY5N2kRI2ailU4j_JBe-yIgjGqE92IEWf-HrUuc5NQa49Ru2ZSjVCxKxEapWDOCF9tnLjpGjmtHPyXBWdquOLOrU9UBfh8RX9lKmxIxCvppIzjoZWvTPl7bq1U3gpe9vK9u__-9D6-f7RHcyBEddSlABzDYrH6Ex4huNs0Yds3scAzD6vDbh-k4LWC8usirX0Rt9-4 |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwEB1V7QE48FFAbCngAwgQWGwSO7YPFVqg1ZZuVxW0Um8msR2pUrttdxeh_jl-GzOOs-VD9NZzHCfKODNv7Jn3AJ7XPndKioJ7LUsulFPc5FXNhfcC419VhsizvTsuhwfi86E8XIKfXS8MlVV2PjE6an_qaI_8HbpaDOXK9PX7s3NOqlF0utpJaFRJWsFvRIqx1NixEy5-YAo329j-hPZ-kedbm_sfhzypDHAncjHnMnjjZL-sDaYyNcliN05UWVYo38jIRlNpqb00tcpDoX1mZIW4XTeu70PREPEBhoAVUQiDyd_Kh83x3pfFOYbUrcRnhrka0aaktp3YvEepKRUA0zlqYSTXf4TGvwPEPye1MQBu3YXbCbmyQbvU7sFSmKzCnYRiWfIRs1W49RvF4X04H7C2Whs9yTEjTFtNWSoKO8ER7NXX0d5r1lGbM8TQDENtW6gwv2BVIk3hFG49o4JW7qfkopmLghRpM5OdBOphPnKzB3BwLQZ4CMuT00l4BCzLnOs3iEmzohFOIdwRXvkyIGyrC--LHmTdF7YucaCTFMexXbA3R6tYtIiNVrG6B28W95y1DCBXjn5JhrPkHnBmV6UuB3w_ItqyA6UNYiLMC3uw3tnWJr8xs5ervAdvO3tfXv7_c9eunu0Z3Bju747saHu88xhu5ojM2vKjdVieT7-HJ4is5vXTtHwZfLvuP-YXoiwwJg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1RT9swED6xIqHtgUHZtLIO_IAE02bRxHbsPFaDqkBBlVilvlmJ7UiToJQ2PPDvd06cjo1tEs9xLlHO5_sud_cdwEFuYyMFZ9QqkVAujaRpnOWUW8vR_2WJq3i2L6-S4YSfT8X0SRd_Ve3epCTrngbP0jQrj-e2OF41vvmwzhfP-hwkSwVVr2Cde6IE3NGTuL_KIwhVj9iMMFbytCWhbebvMn5zTX8e0M8ypZUDGmzBZkCOpF-rehvW3KwNbwOKJMFGl21484RicAfu-6SulkZLviEeU2YLEoqybnEFOboejT-ThlqcIIYl6OrqQoHykWSBtIR6d2eJLyilduGPSGKqgRDhZyK5db6H-IdZvoPJ4PT7tyENYxao4TEvqXA2NaKX5CnGcrmfC14YnkURk7YQFR1PpoSyIs1l7JiyUSoyDFxUYXrWsSJm76E1u5u5D0CiyJhegZgwYgU3EuEGt9ImDmFTzqxlHYiaL6xN4CD3ozBu9Io9udKKRo3oSitadeDL6p55zcDx39WHXnHamydKNlnoMsD380RXui9VipgE47IOdBvd6mC3S40OHQGjTHso6Guj71-X__3c3Zct34eN8clAj86uLj7C6xiBUl0N1IVWuXhwnxDolPletZd_AquQ8hk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+sequential+linear+programming+%28SLP%29+approach+for+uncertainty+analysis-based+data-driven+computational+mechanics&rft.jtitle=Computational+mechanics&rft.au=Huang%2C+Mengcheng&rft.au=Liu%2C+Chang&rft.au=Du%2C+Zongliang&rft.au=Tang%2C+Shan&rft.date=2024-04-01&rft.pub=Springer+Nature+B.V&rft.issn=0178-7675&rft.eissn=1432-0924&rft.volume=73&rft.issue=4&rft.spage=943&rft.epage=965&rft_id=info:doi/10.1007%2Fs00466-023-02395-8 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0178-7675&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0178-7675&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0178-7675&client=summon |