Ensemble learning of multi-kernel Kriging surrogate models using regional discrepancy and space-filling criteria-based hybrid sampling method
Kriging surrogate model has been widely used to simulate expensive models in engineering application. Ensemble of multi-kernel Kriging surrogate models can integrate the information of different kernel functions and enhance the predictive robustness. The performance of the ensemble Kriging model is...
Saved in:
| Published in | Advanced engineering informatics Vol. 58; p. 102186 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2023
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1474-0346 1873-5320 |
| DOI | 10.1016/j.aei.2023.102186 |
Cover
Loading…
| Abstract | Kriging surrogate model has been widely used to simulate expensive models in engineering application. Ensemble of multi-kernel Kriging surrogate models can integrate the information of different kernel functions and enhance the predictive robustness. The performance of the ensemble Kriging model is dependent on the collection of samples. However, the traditional sampling methods primarily concentrate on the accuracy of individual-kernel Kriging, disregarding the difference among different kernel functions. To deal with the issue of sample selection in the context of ensemble Kriging, a regional discrepancy and space-filling criteria-based hybrid sampling (RSHS) method is proposed in this paper. According to the bias-variance decomposition, the expected leave-one-out cross validation error is introduced to compute the weight of each Kriging component. Considering the disagreement of different kernel-based Kriging models, the combination of regional correlation and regional predictive error is derived to measure the regional discrepancy for pursuing local accuracy. The space-filling criterion is also employed to ensure the even coverage across the entire space for global exploration. Therefore, the hybrid sampling criterion is developed to select sample point sequentially by maximizing the regional discrepancy with subject to the constraint of space-filling criterion. The performance of the proposed method is validated by six benchmark examples and an engineering application of airfoil shape model. The results demonstrate the RSHS sampling method can provide promising accuracy and robustness for ensemble Kriging metamodeling. |
|---|---|
| AbstractList | Kriging surrogate model has been widely used to simulate expensive models in engineering application. Ensemble of multi-kernel Kriging surrogate models can integrate the information of different kernel functions and enhance the predictive robustness. The performance of the ensemble Kriging model is dependent on the collection of samples. However, the traditional sampling methods primarily concentrate on the accuracy of individual-kernel Kriging, disregarding the difference among different kernel functions. To deal with the issue of sample selection in the context of ensemble Kriging, a regional discrepancy and space-filling criteria-based hybrid sampling (RSHS) method is proposed in this paper. According to the bias-variance decomposition, the expected leave-one-out cross validation error is introduced to compute the weight of each Kriging component. Considering the disagreement of different kernel-based Kriging models, the combination of regional correlation and regional predictive error is derived to measure the regional discrepancy for pursuing local accuracy. The space-filling criterion is also employed to ensure the even coverage across the entire space for global exploration. Therefore, the hybrid sampling criterion is developed to select sample point sequentially by maximizing the regional discrepancy with subject to the constraint of space-filling criterion. The performance of the proposed method is validated by six benchmark examples and an engineering application of airfoil shape model. The results demonstrate the RSHS sampling method can provide promising accuracy and robustness for ensemble Kriging metamodeling. |
| ArticleNumber | 102186 |
| Author | Li, Bo Fang, Hai Zhang, Zhi Li, Yunhui Shang, Xiaobing |
| Author_xml | – sequence: 1 givenname: Xiaobing surname: Shang fullname: Shang, Xiaobing organization: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China – sequence: 2 givenname: Zhi surname: Zhang fullname: Zhang, Zhi email: zhangzhi1981@hrbeu.edu.cn organization: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China – sequence: 3 givenname: Hai surname: Fang fullname: Fang, Hai organization: Shanghai Electro-mechanical Engineering Institute, Shanghai, China – sequence: 4 givenname: Bo surname: Li fullname: Li, Bo organization: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China – sequence: 5 givenname: Yunhui surname: Li fullname: Li, Yunhui organization: Shanghai Universality, Shanghai, China |
| BookMark | eNp9kEtu2zAQQIkiBZqkPUB3vIBcUl8KWQVB2hQ1kE26JkbkyBmXoowhXcCHyJ0jxV11kdV83wDzrsRFnCMK8VWrjVa6_bbfANKmVGW11KU27QdxqU1XFU1Vqoslr7u6UFXdfhJXKe3Vwpi-uxQv9zHhNASUAYEjxZ2cRzkdQ6biD3LEIH8x7dZ-OjLPO8gop9ljSPKY1jbjjuYIQXpKjvEA0Z0kRC_TARwWI4WwrjmmjExQDJDQy-fTwLTswHR4G0-Yn2f_WXwcIST88i9ei9_f75_uHort44-fd7fbwpV9lwtnoKtQqXFse4BSDY1veqOM6ZwrfW1MO0CnwFV9j6puKtQt1I0blGo1jHVZXYvufNfxnBLjaB1lyMsfmYGC1cquVu3eLlbtatWerS6k_o88ME3Ap3eZmzOzSMO_hGyTI4wOPTG6bP1M79CvsL2VLQ |
| CitedBy_id | crossref_primary_10_1016_j_aei_2024_102675 crossref_primary_10_1002_advs_202403543 crossref_primary_10_1109_ACCESS_2024_3510686 crossref_primary_10_1007_s12205_024_0196_3 crossref_primary_10_1016_j_ress_2024_110226 crossref_primary_10_1080_09544828_2024_2366686 crossref_primary_10_1109_ACCESS_2024_3443139 crossref_primary_10_1016_j_aei_2024_102704 crossref_primary_10_1016_j_aei_2024_102535 |
| Cites_doi | 10.1007/s00158-018-1925-3 10.1609/aaai.v28i1.8904 10.2514/1.J052008 10.1016/j.ins.2022.09.063 10.1016/j.aei.2016.04.004 10.1016/j.ejor.2016.06.041 10.1080/0305215X.2019.1584618 10.1016/j.aei.2022.101689 10.1007/s00158-017-1739-8 10.1007/s00158-018-2053-9 10.1016/j.ress.2021.107611 10.1007/s11831-020-09474-6 10.1016/j.ress.2022.108858 10.1016/j.aei.2021.101430 10.1137/090761811 10.1016/j.compchemeng.2014.05.021 10.1057/palgrave.jors.2601747 10.1016/j.apm.2019.01.040 10.1016/j.aei.2021.101342 10.1080/00401706.2000.10486045 10.1016/j.ifacol.2015.12.183 10.1016/j.compchemeng.2016.10.006 10.1016/j.ress.2017.10.013 10.1002/2015WR016967 10.1007/s10957-013-0442-1 10.1007/s00158-017-1841-y 10.1007/s11831-019-09327-x 10.1007/s00158-008-0338-0 10.1002/qre.945 10.1080/0305215X.2017.1296435 10.1016/j.compchemeng.2017.09.017 10.1080/0305215X.2015.1100470 10.1007/s00158-020-02559-7 10.1115/DETC2002/DAC-34092 10.1007/s00158-021-03001-2 10.2514/1.J054664 10.1016/j.advengsoft.2021.102974 10.2514/1.J058807 10.1016/j.compchemeng.2017.05.025 10.1080/0305215X.2020.1739280 10.1007/s00158-014-1067-1 10.1007/s00158-009-0395-z 10.1007/s00158-013-1028-0 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.aei.2023.102186 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISSN | 1873-5320 |
| ExternalDocumentID | 10_1016_j_aei_2023_102186 S1474034623003142 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ AAAKF AAAKG AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABUCO ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 PC. Q38 RIG RNS ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SSB SSD SST SSV SSZ T5K UHS XPP ZMT ~G- AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH |
| ID | FETCH-LOGICAL-c297t-c8a73e00ff69aa20b5d5980887cc2d4886ba70ac399e0453e16a45cb0061af423 |
| IEDL.DBID | .~1 |
| ISSN | 1474-0346 |
| IngestDate | Tue Jul 01 02:02:41 EDT 2025 Thu Apr 24 23:06:39 EDT 2025 Fri Feb 23 02:36:18 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Kernel function Sampling criterion Space-filling Ensemble learning Kriging |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c297t-c8a73e00ff69aa20b5d5980887cc2d4886ba70ac399e0453e16a45cb0061af423 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_aei_2023_102186 crossref_primary_10_1016_j_aei_2023_102186 elsevier_sciencedirect_doi_10_1016_j_aei_2023_102186 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | October 2023 2023-10-00 |
| PublicationDateYYYYMMDD | 2023-10-01 |
| PublicationDate_xml | – month: 10 year: 2023 text: October 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Advanced engineering informatics |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Sacks, Welch, Mitchell, Wynn (b0220) 1989; 409–423 Zhang, Chowdhury, Zhang, Messac, Castillo (b0100) 2013; 51 Jiang, Shu, Zhou, Zhou, Shao, Xu (b0165) 2015; 48 Shang, Su, Fang, Zeng, Zhang (b0040) 2023; 229 Liu, Ong, Cai (b0125) 2018; 57 Böttcher, Fuchs, Leichsenring, Graf, Kaliske (b0200) 2021; 154 Palar, Shimoyama (b0210) 2019; 59 Hu, Zhou, Jiang, Shao, Xie (b0230) 2018; 50 Chen, Qiu, Jiang, Cai, Gao (b0115) 2018; 57 Crombecq, Gorissen, Deschrijver, Dhaene (b0170) 2011; 33 Liu, Cai, Ong (b0160) 2017; 106 Fang, Lin, Winker, Zhang (b0140) 2000; 42 Lovison, Rigoni (b0150) 2011; 1 Kleijnen (b0225) 2017; 256 Shang, Chao, Ma, Yang (b0130) 2020; 52 R.C. Jin, W. Chen, A. Sudjianto. On sequential sampling for global metamodeling in engineering design. In: ASME 2002 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers. 2002, 2: 539-548. Li, Zhang, Li, Tian, Cheng, Chen, Wang (b0075) 2022; 53 Zhou, Shao, Jiang, Gao, Wang, Shu (b0045) 2016; 30 J.R. Lloyd, D. Duvenaud, R. Grosse, J. Tenenbaum, Z. Ghahramani. Automatic construction and natural-Language description of nonparametric regression models. 28th AAAI Conference on Artificial Intelligence, 2014. Ginsbourger, Helbet, Carraro (b0205) 2008; 24 Palar, Zuhal, Shimoyama (b0060) 2020; 58 Fuhg, Fau, Nackenhorst (b0145) 2021; 28 Liu, Xu, Wang, Meng, Yang (b0110) 2016; 54 Zhang, Wu, Jiang, Choi, Zhou (b0015) 2022; 51 Ben Salem, Tomaso (b0235) 2018; 58 Y. Freund Y, H. Seung. E. Shamir, N. Tishby. Information, prediction, and query by committee. In: Advances in neural information processing systems. 1993, 483-490. Viana, Gogu, Goel (b0025) 2021; 64 Kleijnen, Van Beers (b0190) 2004; 55 Sun, Sun, Li, Zhang, Akhtar (b0135) 2022; 615 Shi, Liu, Long, Liu (b0095) 2016; 48 Eason, Cremaschi (b0195) 2014; 68 Garud, Karimi, Kraft (b0180) 2017; 96 Lee, Choi (b0105) 2014; 50 Acar (b0085) 2014; 49 Zhang, Yue, Qiu, Zhang, Wang (b0120) 2020; 53 Ebrahimi, Jahangirian (b0245) 2014; 162 Goel, Haftka, Shyy, Queipo (b0080) 2007; 33 Gamboa, Klein, Lagnoux, Moreno (b0020) 2021; 212 Faes, Moens (b0010) 2020; 27 (b0050) 2005 Hao, Ye, Jia, Wang, Allen (b0005) 2021; 49 Li, Aute, Azarm (b0175) 2010; 40 Palar, Zuhal, Shimoyama, Tsuchiya (b0240) 2018; 170 Bhosekar, Leraptritou (b0035) 2018; 108 Asher, Croke, Jakeman, Peeters (b0030) 2015; 51 Xu, Ji, Wei, Wang, Yuan (b0070) 2022 Viana, Haftka, Steffen (b0090) 2009; 39 Jin (b0065) 2020; 62 Shi, Lu, Xu, Chen (b0215) 2019; 70 Goel (10.1016/j.aei.2023.102186_b0080) 2007; 33 Lee (10.1016/j.aei.2023.102186_b0105) 2014; 50 Bhosekar (10.1016/j.aei.2023.102186_b0035) 2018; 108 Ebrahimi (10.1016/j.aei.2023.102186_b0245) 2014; 162 Zhou (10.1016/j.aei.2023.102186_b0045) 2016; 30 10.1016/j.aei.2023.102186_b0155 Li (10.1016/j.aei.2023.102186_b0175) 2010; 40 Liu (10.1016/j.aei.2023.102186_b0110) 2016; 54 Faes (10.1016/j.aei.2023.102186_b0010) 2020; 27 Ben Salem (10.1016/j.aei.2023.102186_b0235) 2018; 58 Shi (10.1016/j.aei.2023.102186_b0095) 2016; 48 Lovison (10.1016/j.aei.2023.102186_b0150) 2011; 1 Sacks (10.1016/j.aei.2023.102186_b0220) 1989; 409–423 Palar (10.1016/j.aei.2023.102186_b0060) 2020; 58 10.1016/j.aei.2023.102186_b0185 Eason (10.1016/j.aei.2023.102186_b0195) 2014; 68 Chen (10.1016/j.aei.2023.102186_b0115) 2018; 57 Zhang (10.1016/j.aei.2023.102186_b0120) 2020; 53 Jiang (10.1016/j.aei.2023.102186_b0165) 2015; 48 Kleijnen (10.1016/j.aei.2023.102186_b0225) 2017; 256 Shi (10.1016/j.aei.2023.102186_b0215) 2019; 70 Palar (10.1016/j.aei.2023.102186_b0240) 2018; 170 Gamboa (10.1016/j.aei.2023.102186_b0020) 2021; 212 Liu (10.1016/j.aei.2023.102186_b0160) 2017; 106 Asher (10.1016/j.aei.2023.102186_b0030) 2015; 51 Hu (10.1016/j.aei.2023.102186_b0230) 2018; 50 Li (10.1016/j.aei.2023.102186_b0075) 2022; 53 Ginsbourger (10.1016/j.aei.2023.102186_b0205) 2008; 24 Fuhg (10.1016/j.aei.2023.102186_b0145) 2021; 28 Sun (10.1016/j.aei.2023.102186_b0135) 2022; 615 Kleijnen (10.1016/j.aei.2023.102186_b0190) 2004; 55 Shang (10.1016/j.aei.2023.102186_b0130) 2020; 52 Crombecq (10.1016/j.aei.2023.102186_b0170) 2011; 33 10.1016/j.aei.2023.102186_b0055 Acar (10.1016/j.aei.2023.102186_b0085) 2014; 49 Zhang (10.1016/j.aei.2023.102186_b0100) 2013; 51 Viana (10.1016/j.aei.2023.102186_b0025) 2021; 64 Xu (10.1016/j.aei.2023.102186_b0070) 2022 Garud (10.1016/j.aei.2023.102186_b0180) 2017; 96 Palar (10.1016/j.aei.2023.102186_b0210) 2019; 59 Viana (10.1016/j.aei.2023.102186_b0090) 2009; 39 Zhang (10.1016/j.aei.2023.102186_b0015) 2022; 51 Shang (10.1016/j.aei.2023.102186_b0040) 2023; 229 Jin (10.1016/j.aei.2023.102186_b0065) 2020; 62 Liu (10.1016/j.aei.2023.102186_b0125) 2018; 57 Hao (10.1016/j.aei.2023.102186_b0005) 2021; 49 Fang (10.1016/j.aei.2023.102186_b0140) 2000; 42 Böttcher (10.1016/j.aei.2023.102186_b0200) 2021; 154 (10.1016/j.aei.2023.102186_b0050) 2005 |
| References_xml | – volume: 49 start-page: 969 year: 2014 end-page: 978 ident: b0085 article-title: Simultaneous optimization of shape parameters and weight factors in ensemble of radial basis functions publication-title: Struct. Multidiscip. Optim. – volume: 68 start-page: 220 year: 2014 end-page: 232 ident: b0195 article-title: Adaptive sequential sampling for surrogate model generation with artificial neural networks publication-title: Comput. Chem. Eng. – volume: 70 start-page: 545 year: 2019 end-page: 571 ident: b0215 article-title: An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis publication-title: App. Math. Model. – volume: 57 start-page: 393 year: 2018 end-page: 416 ident: b0125 article-title: A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design publication-title: Struct. Multidiscip. Optim. – volume: 50 start-page: 383 year: 2014 end-page: 394 ident: b0105 article-title: Pointwise ensemble of meta-models using v nearest points cross-validation publication-title: Struct. Multidiscip. Optim. – volume: 50 start-page: 145 year: 2018 end-page: 163 ident: b0230 article-title: An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging publication-title: Eng. Optim. – reference: Y. Freund Y, H. Seung. E. Shamir, N. Tishby. Information, prediction, and query by committee. In: Advances in neural information processing systems. 1993, 483-490. – volume: 30 start-page: 283 year: 2016 end-page: 297 ident: b0045 article-title: An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models publication-title: Adv. Eng. Inf. – volume: 48 start-page: 1202 year: 2016 end-page: 1225 ident: b0095 article-title: An efficient ensemble of radial basis functions method based on quadratic programming publication-title: Eng. Optim. – volume: 212 year: 2021 ident: b0020 article-title: Sensitivity analysis in general metric spaces publication-title: Reliab. Eng. Syst. Saf. – volume: 58 start-page: 1864 year: 2020 end-page: 1880 ident: b0060 article-title: Gaussian process surrogate model with composite kernel learning for engineering design publication-title: AIAA J. – volume: 170 start-page: 175 year: 2018 end-page: 190 ident: b0240 article-title: Global sensitivity analysis via multi-fidelity polynomial chaos expansion publication-title: Reliab. Eng. Syst. Saf. – volume: 64 start-page: 2881 year: 2021 end-page: 2908 ident: b0025 article-title: Surrogate modeling: Tricks that endured the test of time and some recent developments publication-title: Struct. Multidiscip. Optim. – volume: 53 start-page: 474 year: 2020 end-page: 495 ident: b0120 article-title: A unified ensemble of surrogates with global and local measures for global metamodeling publication-title: Eng. Optim. – year: 2022 ident: b0070 article-title: A novel ensemble model using artificial neural network for predicting wave-induced forces on coastal bridge decks publication-title: Eng. Comput. – volume: 40 start-page: 137 year: 2010 end-page: 155 ident: b0175 article-title: An accumulative error based adaptive design of experiments for offline metamodeling publication-title: Structural Multidisciplinary Optimization. – volume: 39 start-page: 439 year: 2009 end-page: 457 ident: b0090 article-title: Multiple surrogates: How cross-validation errors can help us to obtain the best predictor publication-title: Struct. Multidiscip. Optim. – volume: 24 start-page: 681 year: 2008 end-page: 691 ident: b0205 article-title: Discrete mixtures of kernels for Kriging-based optimization publication-title: Qual. Reliab. Eng. Int. – volume: 55 start-page: 876 year: 2004 end-page: 883 ident: b0190 article-title: Application-driven sequential designs for simulation experiments: Kriging metamodelling publication-title: J. Oper. Res. Soc. – volume: 51 start-page: 5957 year: 2015 end-page: 5973 ident: b0030 article-title: A review of surrogate models and their application to groundwater modeling publication-title: Water Resour. Res. – volume: 33 start-page: 1948 year: 2011 end-page: 1974 ident: b0170 article-title: A novel hybrid sequential design strategy for global surrogate modeling of computer experiments publication-title: SIAM J. Sci. Comput. – volume: 615 start-page: 226 year: 2022 end-page: 237 ident: b0135 article-title: Surrogate ensemble assisted large-scale expensive optimization with random grouping publication-title: Inf. Sci. – volume: 256 start-page: 1 year: 2017 end-page: 16 ident: b0225 article-title: Regression and Kriging metamodels with their experimental designs in simulation: A review publication-title: Eur. J. Oper. Res. – volume: 106 start-page: 171 year: 2017 end-page: 182 ident: b0160 article-title: An adaptive sampling approach for Kriging metamodeling by maximizing expected prediction error publication-title: Comput. Chem. Eng. – volume: 108 start-page: 250 year: 2018 end-page: 267 ident: b0035 article-title: Advances in surrogate based modeling, feasibility analysis, and optimization: A review publication-title: Comput. Chem. Eng. – volume: 54 start-page: 3117 year: 2016 end-page: 3133 ident: b0110 article-title: Optimal weighted pointwise ensemble of radial basis functions with different basis functions publication-title: AIAA J. – volume: 57 start-page: 1711 year: 2018 end-page: 1729 ident: b0115 article-title: Ensemble of surrogates with hybrid method using global and local measures for engineering design publication-title: Struct. Multidiscip. Optim. – volume: 51 start-page: 643 year: 2013 end-page: 656 ident: b0100 article-title: Adaptive hybrid surrogate modeling for complex systems publication-title: AIAA J. – volume: 162 start-page: 257 year: 2014 end-page: 271 ident: b0245 article-title: Aerodynamic optimization of airfoils using adaptive parameterization and genetic algorithm publication-title: J. Optim. Theory Appl. – volume: 27 start-page: 633 year: 2020 end-page: 671 ident: b0010 article-title: Recent trends in the modeling and quantification of non-probabilistic uncertainty publication-title: Arch. Comput. Meth. Eng. – volume: 48 start-page: 532 year: 2015 end-page: 537 ident: b0165 article-title: A novel sequential exploration-exploitation sampling strategy for global metamodeling publication-title: IFAC-PapersOnLine. – year: 2005 ident: b0050 publication-title: Gaussian Processes for Machine Learning – volume: 62 start-page: 1313 year: 2020 end-page: 1351 ident: b0065 article-title: Compositional kernel learning using tree-based genetic programming for Gaussian process regression publication-title: Struct. Multidiscip. Optim. – volume: 51 start-page: 101430 year: 2022 ident: b0015 article-title: A multi-fidelity surrogate modeling approach for incorporating multiple non-hierarchical low-fidelity data publication-title: Adv. Eng. Inf. – volume: 96 start-page: 103 year: 2017 end-page: 114 ident: b0180 article-title: Smart sampling algorithm for surrogate model development publication-title: Comput. Chem. Eng. – volume: 409–423 year: 1989 ident: b0220 article-title: Design and analysis of computer experiments publication-title: Stat. Sci. – volume: 1 start-page: 110 year: 2011 end-page: 126 ident: b0150 article-title: Adaptive sampling with a Lipschitz criterion for accurate metamodeling publication-title: Communications in Applied and Industrial Mathematics. – volume: 229 start-page: 108858 year: 2023 ident: b0040 article-title: An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis publication-title: Reliab. Eng. Syst. Saf. – volume: 53 start-page: 101689 year: 2022 ident: b0075 article-title: On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks publication-title: Adv. Eng. Inf. – reference: R.C. Jin, W. Chen, A. Sudjianto. On sequential sampling for global metamodeling in engineering design. In: ASME 2002 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers. 2002, 2: 539-548. – volume: 154 start-page: 102974 year: 2021 ident: b0200 article-title: ELSA: An efficient, adaptive Ensemble Learning-based Sampling Approach publication-title: Advances in Engineering Softwares. – volume: 28 start-page: 2689 year: 2021 end-page: 2747 ident: b0145 article-title: State-of-the-art and comparative review of adaptive sampling methods for Kriging publication-title: Archives Computational Methods in Engineering. – volume: 49 start-page: 101342 year: 2021 ident: b0005 article-title: Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge publication-title: Adv. Eng. Inf. – reference: J.R. Lloyd, D. Duvenaud, R. Grosse, J. Tenenbaum, Z. Ghahramani. Automatic construction and natural-Language description of nonparametric regression models. 28th AAAI Conference on Artificial Intelligence, 2014. – volume: 33 start-page: 199 year: 2007 end-page: 216 ident: b0080 article-title: Ensemble of surrogates. Structural Multidisciplinary publication-title: Optimization – volume: 42 start-page: 237 year: 2000 end-page: 248 ident: b0140 article-title: Uniform Design: Theory and Application publication-title: Technometrics – volume: 59 start-page: 93 year: 2019 end-page: 116 ident: b0210 article-title: Efficient global optimization with ensemble and selection of kernel functions for engineering design publication-title: Struct. Multidiscip. Optim. – volume: 58 start-page: 719 year: 2018 end-page: 734 ident: b0235 article-title: Automatic selection for general surrogate models publication-title: Struct. Multidiscip. Optim. – volume: 52 start-page: 271 year: 2020 end-page: 287 ident: b0130 article-title: An efficient local search-based genetic algorithm for constructing optimal Latin hypercube design publication-title: Eng. Optim. – volume: 33 start-page: 199 issue: 3 year: 2007 ident: 10.1016/j.aei.2023.102186_b0080 article-title: Ensemble of surrogates. Structural Multidisciplinary publication-title: Optimization – volume: 58 start-page: 719 issue: 2 year: 2018 ident: 10.1016/j.aei.2023.102186_b0235 article-title: Automatic selection for general surrogate models publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-018-1925-3 – ident: 10.1016/j.aei.2023.102186_b0055 doi: 10.1609/aaai.v28i1.8904 – volume: 51 start-page: 643 issue: 3 year: 2013 ident: 10.1016/j.aei.2023.102186_b0100 article-title: Adaptive hybrid surrogate modeling for complex systems publication-title: AIAA J. doi: 10.2514/1.J052008 – year: 2005 ident: 10.1016/j.aei.2023.102186_b0050 – volume: 615 start-page: 226 year: 2022 ident: 10.1016/j.aei.2023.102186_b0135 article-title: Surrogate ensemble assisted large-scale expensive optimization with random grouping publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.09.063 – volume: 30 start-page: 283 issue: 3 year: 2016 ident: 10.1016/j.aei.2023.102186_b0045 article-title: An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2016.04.004 – volume: 256 start-page: 1 issue: 1 year: 2017 ident: 10.1016/j.aei.2023.102186_b0225 article-title: Regression and Kriging metamodels with their experimental designs in simulation: A review publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2016.06.041 – volume: 52 start-page: 271 issue: 2 year: 2020 ident: 10.1016/j.aei.2023.102186_b0130 article-title: An efficient local search-based genetic algorithm for constructing optimal Latin hypercube design publication-title: Eng. Optim. doi: 10.1080/0305215X.2019.1584618 – volume: 53 start-page: 101689 year: 2022 ident: 10.1016/j.aei.2023.102186_b0075 article-title: On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2022.101689 – volume: 57 start-page: 393 issue: 1 year: 2018 ident: 10.1016/j.aei.2023.102186_b0125 article-title: A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-017-1739-8 – volume: 1 start-page: 110 issue: 2 year: 2011 ident: 10.1016/j.aei.2023.102186_b0150 article-title: Adaptive sampling with a Lipschitz criterion for accurate metamodeling publication-title: Communications in Applied and Industrial Mathematics. – volume: 59 start-page: 93 issue: 1 year: 2019 ident: 10.1016/j.aei.2023.102186_b0210 article-title: Efficient global optimization with ensemble and selection of kernel functions for engineering design publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-018-2053-9 – volume: 212 year: 2021 ident: 10.1016/j.aei.2023.102186_b0020 article-title: Sensitivity analysis in general metric spaces publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.107611 – volume: 28 start-page: 2689 issue: 4 year: 2021 ident: 10.1016/j.aei.2023.102186_b0145 article-title: State-of-the-art and comparative review of adaptive sampling methods for Kriging publication-title: Archives Computational Methods in Engineering. doi: 10.1007/s11831-020-09474-6 – volume: 229 start-page: 108858 year: 2023 ident: 10.1016/j.aei.2023.102186_b0040 article-title: An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2022.108858 – volume: 51 start-page: 101430 year: 2022 ident: 10.1016/j.aei.2023.102186_b0015 article-title: A multi-fidelity surrogate modeling approach for incorporating multiple non-hierarchical low-fidelity data publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2021.101430 – volume: 33 start-page: 1948 issue: 4 year: 2011 ident: 10.1016/j.aei.2023.102186_b0170 article-title: A novel hybrid sequential design strategy for global surrogate modeling of computer experiments publication-title: SIAM J. Sci. Comput. doi: 10.1137/090761811 – volume: 68 start-page: 220 year: 2014 ident: 10.1016/j.aei.2023.102186_b0195 article-title: Adaptive sequential sampling for surrogate model generation with artificial neural networks publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2014.05.021 – volume: 55 start-page: 876 issue: 8 year: 2004 ident: 10.1016/j.aei.2023.102186_b0190 article-title: Application-driven sequential designs for simulation experiments: Kriging metamodelling publication-title: J. Oper. Res. Soc. doi: 10.1057/palgrave.jors.2601747 – volume: 70 start-page: 545 year: 2019 ident: 10.1016/j.aei.2023.102186_b0215 article-title: An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis publication-title: App. Math. Model. doi: 10.1016/j.apm.2019.01.040 – volume: 49 start-page: 101342 year: 2021 ident: 10.1016/j.aei.2023.102186_b0005 article-title: Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2021.101342 – volume: 42 start-page: 237 issue: 3 year: 2000 ident: 10.1016/j.aei.2023.102186_b0140 article-title: Uniform Design: Theory and Application publication-title: Technometrics doi: 10.1080/00401706.2000.10486045 – volume: 48 start-page: 532 issue: 28 year: 2015 ident: 10.1016/j.aei.2023.102186_b0165 article-title: A novel sequential exploration-exploitation sampling strategy for global metamodeling publication-title: IFAC-PapersOnLine. doi: 10.1016/j.ifacol.2015.12.183 – volume: 96 start-page: 103 year: 2017 ident: 10.1016/j.aei.2023.102186_b0180 article-title: Smart sampling algorithm for surrogate model development publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2016.10.006 – volume: 170 start-page: 175 year: 2018 ident: 10.1016/j.aei.2023.102186_b0240 article-title: Global sensitivity analysis via multi-fidelity polynomial chaos expansion publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2017.10.013 – year: 2022 ident: 10.1016/j.aei.2023.102186_b0070 article-title: A novel ensemble model using artificial neural network for predicting wave-induced forces on coastal bridge decks publication-title: Eng. Comput. – volume: 51 start-page: 5957 issue: 8 year: 2015 ident: 10.1016/j.aei.2023.102186_b0030 article-title: A review of surrogate models and their application to groundwater modeling publication-title: Water Resour. Res. doi: 10.1002/2015WR016967 – volume: 162 start-page: 257 issue: 1 year: 2014 ident: 10.1016/j.aei.2023.102186_b0245 article-title: Aerodynamic optimization of airfoils using adaptive parameterization and genetic algorithm publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-013-0442-1 – volume: 57 start-page: 1711 issue: 4 year: 2018 ident: 10.1016/j.aei.2023.102186_b0115 article-title: Ensemble of surrogates with hybrid method using global and local measures for engineering design publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-017-1841-y – ident: 10.1016/j.aei.2023.102186_b0185 – volume: 27 start-page: 633 issue: 3 year: 2020 ident: 10.1016/j.aei.2023.102186_b0010 article-title: Recent trends in the modeling and quantification of non-probabilistic uncertainty publication-title: Arch. Comput. Meth. Eng. doi: 10.1007/s11831-019-09327-x – volume: 39 start-page: 439 issue: 4 year: 2009 ident: 10.1016/j.aei.2023.102186_b0090 article-title: Multiple surrogates: How cross-validation errors can help us to obtain the best predictor publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-008-0338-0 – volume: 409–423 year: 1989 ident: 10.1016/j.aei.2023.102186_b0220 article-title: Design and analysis of computer experiments publication-title: Stat. Sci. – volume: 24 start-page: 681 issue: 6 year: 2008 ident: 10.1016/j.aei.2023.102186_b0205 article-title: Discrete mixtures of kernels for Kriging-based optimization publication-title: Qual. Reliab. Eng. Int. doi: 10.1002/qre.945 – volume: 50 start-page: 145 issue: 1 year: 2018 ident: 10.1016/j.aei.2023.102186_b0230 article-title: An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging publication-title: Eng. Optim. doi: 10.1080/0305215X.2017.1296435 – volume: 108 start-page: 250 year: 2018 ident: 10.1016/j.aei.2023.102186_b0035 article-title: Advances in surrogate based modeling, feasibility analysis, and optimization: A review publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.09.017 – volume: 48 start-page: 1202 issue: 7 year: 2016 ident: 10.1016/j.aei.2023.102186_b0095 article-title: An efficient ensemble of radial basis functions method based on quadratic programming publication-title: Eng. Optim. doi: 10.1080/0305215X.2015.1100470 – volume: 62 start-page: 1313 issue: 3 year: 2020 ident: 10.1016/j.aei.2023.102186_b0065 article-title: Compositional kernel learning using tree-based genetic programming for Gaussian process regression publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-020-02559-7 – ident: 10.1016/j.aei.2023.102186_b0155 doi: 10.1115/DETC2002/DAC-34092 – volume: 64 start-page: 2881 issue: 5 year: 2021 ident: 10.1016/j.aei.2023.102186_b0025 article-title: Surrogate modeling: Tricks that endured the test of time and some recent developments publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-021-03001-2 – volume: 54 start-page: 3117 issue: 10 year: 2016 ident: 10.1016/j.aei.2023.102186_b0110 article-title: Optimal weighted pointwise ensemble of radial basis functions with different basis functions publication-title: AIAA J. doi: 10.2514/1.J054664 – volume: 154 start-page: 102974 year: 2021 ident: 10.1016/j.aei.2023.102186_b0200 article-title: ELSA: An efficient, adaptive Ensemble Learning-based Sampling Approach publication-title: Advances in Engineering Softwares. doi: 10.1016/j.advengsoft.2021.102974 – volume: 58 start-page: 1864 issue: 4 year: 2020 ident: 10.1016/j.aei.2023.102186_b0060 article-title: Gaussian process surrogate model with composite kernel learning for engineering design publication-title: AIAA J. doi: 10.2514/1.J058807 – volume: 106 start-page: 171 year: 2017 ident: 10.1016/j.aei.2023.102186_b0160 article-title: An adaptive sampling approach for Kriging metamodeling by maximizing expected prediction error publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.05.025 – volume: 53 start-page: 474 issue: 3 year: 2020 ident: 10.1016/j.aei.2023.102186_b0120 article-title: A unified ensemble of surrogates with global and local measures for global metamodeling publication-title: Eng. Optim. doi: 10.1080/0305215X.2020.1739280 – volume: 50 start-page: 383 issue: 3 year: 2014 ident: 10.1016/j.aei.2023.102186_b0105 article-title: Pointwise ensemble of meta-models using v nearest points cross-validation publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-014-1067-1 – volume: 40 start-page: 137 issue: 1-6 year: 2010 ident: 10.1016/j.aei.2023.102186_b0175 article-title: An accumulative error based adaptive design of experiments for offline metamodeling publication-title: Structural Multidisciplinary Optimization. doi: 10.1007/s00158-009-0395-z – volume: 49 start-page: 969 issue: 6 year: 2014 ident: 10.1016/j.aei.2023.102186_b0085 article-title: Simultaneous optimization of shape parameters and weight factors in ensemble of radial basis functions publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-013-1028-0 |
| SSID | ssj0016897 |
| Score | 2.4389613 |
| Snippet | Kriging surrogate model has been widely used to simulate expensive models in engineering application. Ensemble of multi-kernel Kriging surrogate models can... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 102186 |
| SubjectTerms | Ensemble learning Kernel function Kriging Sampling criterion Space-filling |
| Title | Ensemble learning of multi-kernel Kriging surrogate models using regional discrepancy and space-filling criteria-based hybrid sampling method |
| URI | https://dx.doi.org/10.1016/j.aei.2023.102186 |
| Volume | 58 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV09T-QwELUQNDR8n45PTUGFZNbZOE5cIgRaQNAAEl3k2M6yx5JFyVJcc__g_vPNJA7iJKCgTGJLkWc88xK_mcfYoYljb6VWHNG45dLhB4pR9BsOof0wNcpoSye61zdqdC8vH5KHBXba18IQrTLE_i6mt9E63BmE1Ry8TCaD20imUsQS8zd5pqQ4TN3r0KeP_7zRPCKVdQIrOJjT6P5ks-V4GT85Jv1wamAQUTn1R7npXb45X2MrASjCSfcu62zBVxtsNYBGCFuy2WR_z6rGPxdTD0EAYgyzElqeIH_ydeWncNWKX42hea3rGf02g1b_pgEivY-BtBkIjwNV6NaYnjDegqkcYLCxnpeTtm03YHihvs6GU-Jz8Pibar2gMURJx8edFPUWuz8_uzsd8aCxwO1Qp3NuM5PGXoiyVNqYoSgSl-iMQo-1Q4e7WxUmFcai7Tyiv9hHysjE0maNTIlY7AdbrGaV_8lAC-0SW8Y-QVAjvDZx5jKrC1cKlykht5noVze3oQE56WBM855p9itHg-RkkLwzyDY7epvy0nXf-Gqw7E2W_-dCOWaHz6ftfG_aLlumq47Xt8cW5_Wr30d8Mi8OWgc8YEsnF1ejm389peXk |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3BTtwwELXQcmgvtLQgtpR2Dj0hmfVuHCc-IgRaurAXQOIWObazLCxZlCwHPoJ_ZiZxEJVaDr3GGSny2G9e7Jl5jP0yUeSt1IojG7dcOvxBMYqO4ZDajxKjjLZ0o3s-VeMr-fs6vl5jR10tDKVVBuxvMb1B6_BkEGZz8DCfDy6GMpEikhi_aWVKxOF16k4le2z98HQynr5eJqi01VjBMU4G3eVmk-Zl_PyAJMSph8GQKqr_Fp7ehJyTz2wjcEU4bD9nk6358gv7FHgjhF1Zf2XPx2Xt7_OFh6ABMYNlAU2qIL_zVekXMGn0r2ZQP1bVkk7OoJHAqYHy3mdA8gxEyYGKdCuMUAi5YEoHiDfW82LedO4GRBhq7Ww4xT4HN09U7gW1oax0HG7VqLfY1cnx5dGYB5kFbkc6WXGbmiTyQhSF0saMRB67WKeEPtaOHG5wlZtEGIvu80gAIz9URsaW9uvQFEjHtlmvXJZ-h4EW2sW2iHyMvEZ4baLUpVbnrhAuVUL2mehmN7OhBzlJYSyyLtnsNkOHZOSQrHVIn-2_mjy0DTjee1l2Lsv-WEUZBoh_m337P7Of7MP48vwsOzudTnbZRxpp0_y-s96qevR7SFdW-Y-wHF8AIWbolQ |
| 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=Ensemble+learning+of+multi-kernel+Kriging+surrogate+models+using+regional+discrepancy+and+space-filling+criteria-based+hybrid+sampling+method&rft.jtitle=Advanced+engineering+informatics&rft.au=Shang%2C+Xiaobing&rft.au=Zhang%2C+Zhi&rft.au=Fang%2C+Hai&rft.au=Li%2C+Bo&rft.date=2023-10-01&rft.pub=Elsevier+Ltd&rft.issn=1474-0346&rft.eissn=1873-5320&rft.volume=58&rft_id=info:doi/10.1016%2Fj.aei.2023.102186&rft.externalDocID=S1474034623003142 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1474-0346&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1474-0346&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1474-0346&client=summon |