A Python surrogate modeling framework with derivatives

The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Advances in engineering software (1992) Ročník 135; s. 102662
Hlavní autoři:	Bouhlel, Mohamed Amine, Hwang, John T., Bartoli, Nathalie, Lafage, Rémi, Morlier, Joseph, Martins, Joaquim R.R.A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.09.2019 Elsevier
Témata:	Computer Science Derivatives Engineering Sciences Gradient-enhanced surrogate modeling Mathematics Physics Surrogate modeling Surrogate modeling Gradient-enhanced surrogate modeling Derivatives SAMPLING METAMODELING GRADIENT-ENHANCED SURROGATE MODELING MODELE REDUIT BASE SUR LE GRADIENT SURROGATE MODELING MODELE REDUIT ECHANTILLONNAGE DERIVATIVES DERIVEES METAMODELE
ISSN:	0965-9978
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy-minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository.11https://github.com/SMTorg/SMT.
AbstractList	The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy-minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository. La toolbox (SMT) est une librairie de python qui contient une collection de modèles réduits, de techniques d'échantillonnage, et des fonctions d'évaluation. Ceci vise à fournir une bibliothèque simple à utiliser pour des modèles réduits. SMT est différente des librairies existantes modelant des bibliothèques car elle met l’accent sur la connaissance des dérivées. Elle inclut également les nouveaux modèles réduits qui ne sont pas disponibles ailleurs : krigeage combiné aux moindres carrés partiels et interpolation par spline basée sur une minimisation d’énergie. SMT est documentée et distribuée sous la licence New BSD de schéma et peut être téléchargée via https://github.COM/SMTorg/SMT. The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy-minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository.11https://github.com/SMTorg/SMT.
ArticleNumber	102662
Author	Lafage, Rémi Martins, Joaquim R.R.A. Bouhlel, Mohamed Amine Hwang, John T. Morlier, Joseph Bartoli, Nathalie
Author_xml	– sequence: 1 givenname: Mohamed Amine orcidid: 0000-0003-2182-3340 surname: Bouhlel fullname: Bouhlel, Mohamed Amine email: mbouhlel@umich.edu organization: Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA – sequence: 2 givenname: John T. surname: Hwang fullname: Hwang, John T. email: jhwang@eng.ucsd.edu organization: Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA – sequence: 3 givenname: Nathalie orcidid: 0000-0002-6451-2203 surname: Bartoli fullname: Bartoli, Nathalie email: nathalie.bartoli@onera.fr organization: ONERA/DTIS, Université de Toulouse, Toulouse, France – sequence: 4 givenname: Rémi surname: Lafage fullname: Lafage, Rémi email: remi.lafage@onera.fr organization: ONERA/DTIS, Université de Toulouse, Toulouse, France – sequence: 5 givenname: Joseph surname: Morlier fullname: Morlier, Joseph email: joseph.morlier@isae-supaero.fr organization: ICA, Université de Toulouse, ISAE–SUPAERO, INSA, CNRS, MINES ALBI, UPS, Toulouse, France – sequence: 6 givenname: Joaquim R.R.A. orcidid: 0000-0003-2143-1478 surname: Martins fullname: Martins, Joaquim R.R.A. email: jrram@umich.edu organization: Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA
BackLink	https://hal.science/hal-02294310$$DView record in HAL
BookMark	eNqNkE1LAzEQhnOoYFv9D3v1sOsk-5mLUItaoaAHPYc0mbSp240kcUv_vVuqCF70NDC87zPDMyGjznVISEIho0Cr620mdY_dOjgTMwaUZ5BnAOWIjIFXZcp53ZyTSQhbAFoAo2NSzZLnQ9y4Lgkf3ru1jJjsnMbWduvEeLnDvfNvyd7GTaLR215G22O4IGdGtgEvv-aUvN7fvcwX6fLp4XE-W6ZqwMe0Rs7zJueNrCVoio02hmrgtFrRShWmkcyUXDWqLgqFjK_KnLMKjVFaU8NUPiVXJ-5GtuLd2530B-GkFYvZUhx3wBgvcgo9HbI3p6zyLgSPRigbh3ddF720raAgjpbEVvxYEkdLAnIxWBoAzS_A98V_VG9PVRxk9Ba9CMpip1BbjyoK7ezfkE9cHY18
CitedBy_id	crossref_primary_10_1371_journal_pone_0266233 crossref_primary_10_3390_aerospace7070087 crossref_primary_10_1016_j_apenergy_2022_120357 crossref_primary_10_1007_s11081_022_09731_6 crossref_primary_10_3847_1538_4357_ac6606 crossref_primary_10_1186_s10033_023_00857_x crossref_primary_10_1007_s00158_024_03906_8 crossref_primary_10_1088_1475_7516_2021_12_046 crossref_primary_10_1108_RPJ_02_2024_0068 crossref_primary_10_1007_s00158_019_02387_4 crossref_primary_10_1007_s00158_021_03001_2 crossref_primary_10_1017_dce_2024_56 crossref_primary_10_1016_j_uclim_2024_101831 crossref_primary_10_1371_journal_pcbi_1010533 crossref_primary_10_2514_1_J063920 crossref_primary_10_3233_ISP_230013 crossref_primary_10_1016_j_ins_2022_04_041 crossref_primary_10_3390_aerospace12070644 crossref_primary_10_1016_j_renene_2024_120115 crossref_primary_10_1016_j_nucengdes_2022_111968 crossref_primary_10_1016_j_ast_2023_108673 crossref_primary_10_1016_j_firesaf_2020_103175 crossref_primary_10_1016_j_applthermaleng_2022_119831 crossref_primary_10_1016_j_swevo_2023_101252 crossref_primary_10_1107_S1600576723010014 crossref_primary_10_1016_j_asoc_2022_109957 crossref_primary_10_1007_s00158_025_03998_w crossref_primary_10_1016_j_camwa_2020_02_010 crossref_primary_10_1108_EC_06_2023_0247 crossref_primary_10_1016_j_euromechflu_2024_10_008 crossref_primary_10_1016_j_ifacol_2020_12_316 crossref_primary_10_1016_j_compchemeng_2024_108584 crossref_primary_10_1108_HFF_09_2022_0553 crossref_primary_10_3847_1538_4357_ac58fd crossref_primary_10_1007_s00170_024_13489_9 crossref_primary_10_1016_j_buildenv_2023_111157 crossref_primary_10_1016_j_sna_2022_113949 crossref_primary_10_2514_1_J063533 crossref_primary_10_1088_1742_6596_2507_1_012009 crossref_primary_10_1029_2023MS003792 crossref_primary_10_1016_j_neucom_2023_126472 crossref_primary_10_1016_j_cma_2024_116913 crossref_primary_10_1016_j_paerosci_2021_100714 crossref_primary_10_3847_PSJ_ac38ac crossref_primary_10_2514_1_I011614 crossref_primary_10_1038_s43247_025_02366_2 crossref_primary_10_1016_j_anucene_2021_108494 crossref_primary_10_1016_j_ast_2021_106701 crossref_primary_10_1007_s00158_024_03817_8 crossref_primary_10_1016_j_procir_2024_10_069 crossref_primary_10_1088_1755_1315_1337_1_012019 crossref_primary_10_3390_agriculture14020167 crossref_primary_10_1016_j_ast_2024_109383 crossref_primary_10_3390_aerospace7100152 crossref_primary_10_1007_s00158_021_03016_9 crossref_primary_10_2514_1_J061603 crossref_primary_10_1016_j_compchemeng_2023_108517 crossref_primary_10_2514_1_J059254 crossref_primary_10_1007_s10589_024_00563_x crossref_primary_10_1109_TAP_2025_3534987 crossref_primary_10_1016_j_ast_2022_107388 crossref_primary_10_3390_app13116361 crossref_primary_10_1016_j_cma_2023_116687 crossref_primary_10_1103_PhysRevResearch_5_L012021 crossref_primary_10_1016_j_advengsoft_2023_103571 crossref_primary_10_1080_19942060_2025_2493071 crossref_primary_10_1016_j_neunet_2022_06_025 crossref_primary_10_1007_s12145_024_01361_z crossref_primary_10_1007_s11431_024_2764_5 crossref_primary_10_1007_s00158_023_03736_0 crossref_primary_10_2514_1_C037899 crossref_primary_10_1016_j_asoc_2023_110744 crossref_primary_10_1109_TSMC_2024_3519537 crossref_primary_10_1002_nme_7498 crossref_primary_10_1016_j_jcp_2022_111595 crossref_primary_10_1007_s42405_022_00476_1 crossref_primary_10_3390_aerospace8010010 crossref_primary_10_1007_s00366_024_02035_6 crossref_primary_10_1029_2021MS002843 crossref_primary_10_1007_s41019_022_00193_5 crossref_primary_10_1016_j_cma_2023_116453 crossref_primary_10_1007_s11740_022_01174_3 crossref_primary_10_1016_j_enconman_2022_116033 crossref_primary_10_1016_j_ijmst_2025_02_003 crossref_primary_10_2514_1_C038291 crossref_primary_10_1016_j_jhydrol_2023_129496 crossref_primary_10_2514_1_J061032 crossref_primary_10_3390_aerospace11040260 crossref_primary_10_1016_j_renene_2023_05_054 crossref_primary_10_3390_atmos13030444 crossref_primary_10_1016_j_actaastro_2020_12_018 crossref_primary_10_3390_math11132937 crossref_primary_10_1016_j_ast_2020_105980 crossref_primary_10_1109_TITS_2021_3130040 crossref_primary_10_1007_s00500_022_07362_8 crossref_primary_10_3390_e25030410 crossref_primary_10_1007_s00158_023_03620_x crossref_primary_10_1007_s00170_022_10260_w crossref_primary_10_1287_ijoc_2023_0250 crossref_primary_10_3390_drones6100307 crossref_primary_10_1017_dce_2024_29 crossref_primary_10_1080_01605682_2024_2415474 crossref_primary_10_1093_mnras_stab661 crossref_primary_10_3390_aerospace9100594 crossref_primary_10_1017_aer_2023_66 crossref_primary_10_2514_1_J063222 crossref_primary_10_1088_1742_6596_2265_3_032095 crossref_primary_10_1007_s00158_024_03785_z crossref_primary_10_1016_j_ast_2021_107309 crossref_primary_10_1016_j_ymssp_2023_110535 crossref_primary_10_2514_1_J063976 crossref_primary_10_2514_1_J059921 crossref_primary_10_1002_nme_6981 crossref_primary_10_1016_j_applthermaleng_2023_121453 crossref_primary_10_1007_s00158_020_02646_9 crossref_primary_10_1093_mnras_stad2814 crossref_primary_10_1016_j_ymssp_2023_110255 crossref_primary_10_1002_aic_17656 crossref_primary_10_1016_j_engfracmech_2022_108303 crossref_primary_10_1016_j_tust_2023_105315 crossref_primary_10_1016_j_ast_2024_109331 crossref_primary_10_1038_s41598_024_73323_w crossref_primary_10_1016_j_ast_2023_108846 crossref_primary_10_2514_1_B38696 crossref_primary_10_1007_s00158_021_03048_1 crossref_primary_10_1016_j_ast_2021_106984 crossref_primary_10_1016_j_ast_2022_107357 crossref_primary_10_1007_s11998_020_00352_1 crossref_primary_10_1016_j_ast_2023_108843 crossref_primary_10_1016_j_apenergy_2024_123130 crossref_primary_10_1007_s11047_020_09820_4 crossref_primary_10_3390_s24155057 crossref_primary_10_2514_1_J062611 crossref_primary_10_1088_2632_2153_acb2b3 crossref_primary_10_3390_app12031633 crossref_primary_10_1016_j_swevo_2024_101813 crossref_primary_10_1016_j_ress_2021_108139 crossref_primary_10_1016_j_eswa_2025_127472 crossref_primary_10_3390_app10062075 crossref_primary_10_1016_j_ijheatfluidflow_2023_109112 crossref_primary_10_1007_s42235_021_0069_0 crossref_primary_10_1038_s42005_022_00977_1 crossref_primary_10_1007_s00158_020_02488_5 crossref_primary_10_1016_j_engappai_2024_108118 crossref_primary_10_1016_j_envsoft_2023_105825 crossref_primary_10_1016_j_icarus_2023_115432 crossref_primary_10_1080_19942060_2025_2499131 crossref_primary_10_1016_j_firesaf_2022_103591 crossref_primary_10_1016_j_chroma_2022_463408 crossref_primary_10_1016_j_advengsoft_2020_102900 crossref_primary_10_1088_3049_4761_adf6bf crossref_primary_10_1007_s00158_021_02977_1 crossref_primary_10_1002_aic_17358 crossref_primary_10_1016_j_ast_2023_108185 crossref_primary_10_1016_j_ress_2023_109393 crossref_primary_10_1109_TITS_2023_3335104 crossref_primary_10_1177_09544100231205154 crossref_primary_10_3390_aerospace11080669 crossref_primary_10_1016_j_ijheatfluidflow_2023_109242 crossref_primary_10_1007_s10898_024_01443_8
Cites_doi	10.1016/j.ast.2017.12.030 10.2514/1.C032150 10.2514/1.J051895 10.1007/s00158-019-02211-z 10.2514/1.C034967 10.1006/jcom.2001.0588 10.1115/1.4029219 10.1080/0305215X.2017.1419344 10.1007/s00366-018-0590-x 10.1016/j.ast.2019.03.041 10.2514/6.2013-2581 10.2514/1.C035082 10.1007/s00158-015-1395-9 10.1007/s00158-010-0554-2 10.1080/00401706.1993.10485320 10.1145/3182393 10.1016/j.jspi.2004.02.014 10.1007/s00158-012-0763-y 10.1080/00401706.1989.10488474 10.1155/2016/6723410 10.2514/1.29123 10.2514/1.J054154 10.2514/1.J052184 10.1016/j.ast.2012.01.006 10.1162/evco.2006.14.1.119
ContentType	Journal Article
Copyright	2019 licence_http://creativecommons.org/publicdomain/zero
Copyright_xml	– notice: 2019 – notice: licence_http://creativecommons.org/publicdomain/zero
DBID	AAYXX CITATION 1XC VOOES
DOI	10.1016/j.advengsoft.2019.03.005
DatabaseName	CrossRef Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access)
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences Engineering Computer Science Mathematics Physics
ExternalDocumentID	oai:HAL:hal-02294310v1 10_1016_j_advengsoft_2019_03_005 S0965997818309360
GroupedDBID	--K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 1XC VOOES
ID	FETCH-LOGICAL-c402t-7e9938398a7a0d1e8dff1d0916b16c4f8a2f59c8c744ce29b53926effcdd1f2c3
ISICitedReferencesCount	256
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000502030600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0965-9978
IngestDate	Tue Nov 25 06:21:16 EST 2025 Sat Nov 29 07:07:04 EST 2025 Tue Nov 18 21:51:13 EST 2025 Fri Feb 23 02:27:54 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Surrogate modeling Gradient-enhanced surrogate modeling Derivatives SAMPLING METAMODELING GRADIENT-ENHANCED SURROGATE MODELING MODELE REDUIT BASE SUR LE GRADIENT SURROGATE MODELING MODELE REDUIT ECHANTILLONNAGE DERIVATIVES DERIVEES METAMODELE
Language	English
License	licence_http://creativecommons.org/publicdomain/zero/: http://creativecommons.org/publicdomain/zero
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c402t-7e9938398a7a0d1e8dff1d0916b16c4f8a2f59c8c744ce29b53926effcdd1f2c3
ORCID	0000-0003-2182-3340 0000-0002-6451-2203 0000-0003-2143-1478 0000-0002-1511-2086
OpenAccessLink	https://hal.science/hal-02294310
ParticipantIDs	hal_primary_oai_HAL_hal_02294310v1 crossref_citationtrail_10_1016_j_advengsoft_2019_03_005 crossref_primary_10_1016_j_advengsoft_2019_03_005 elsevier_sciencedirect_doi_10_1016_j_advengsoft_2019_03_005
PublicationCentury	2000
PublicationDate	2019-09-01
PublicationDateYYYYMMDD	2019-09-01
PublicationDate_xml	– month: 09 year: 2019 text: 2019-09-01 day: 01
PublicationDecade	2010
PublicationTitle	Advances in engineering software (1992)
PublicationYear	2019
Publisher	Elsevier Ltd Elsevier
Publisher_xml	– name: Elsevier Ltd – name: Elsevier
References	Gray, Hwang, Martins, Moore, Naylor (bib0016) 2019 Li, Bouhlel, Martins (bib0014) 2019; 57 Bouhlel, Bartoli, Otsmane, Morlier (bib0004) 2016; 53 Shepard (bib0011) 1968 Martins, Lambe (bib0017) 2013; 51 Han, Görtz, Zimmermann (bib0021) 2013; 25 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel (bib0001) 2011; 12 Liping, Don, Gene, Mahidhar (bib0038) 2006 Bouhlel, Martins (bib0006) 2019; 1 Bouhlel, Bartoli, Regis, Otsmane, Morlier (bib0033) 2018; 50 Lyu Z, Kenway GK, Paige C, Martins JRRA. Automatic differentiation adjoint of the Reynolds-averaged Navier–Stokes equations with a turbulence model. In: Proceedings of the twenty-first AIAA computational fluid dynamics conference. San Diego, CA; doi Powell (bib0013) 1994 Martins (bib0028) 2016; Green Aviation Hwang, Jasa, Martins (bib0030) 2019 An, Owen (bib0023) 2001; 17 Burdette, Martins (bib0029) 2019; 56 Jin, Chen, Sudjianto (bib0008) 2005; 134 Powell (bib0010) 1992 Bartoli, Lefebvre, Dubreuil, Olivanti, Priem, Bons, Martins, Morlier (bib0015) 2019 Mader, Martins, Alonso, van der Weide (bib0024) 2008; 46 Hastie, Tibshirani, Friedman (bib0012) 2001 Kenway, Martins (bib0026) 2016; 54 Martins, Hwang (bib0018) 2013; 51 Noesis Solutions. OPTIMUS. 2009. Rasmussen, Williams (bib0003) 2006 Lambe, Martins (bib0031) 2012; 46 . Morris, Mitchell, Ylvisaker (bib0039) 1993; 35 Le Gratiet (bib0022) 2013 Sacks, Schiller, Welch (bib0009) 1989; 31 Bettebghor, Bartoli, Grihon, Morlier, Samuelides (bib0020) 2011; 43 Hwang, Ning (bib0034) 2018 Shang, Qiu (bib0036) 2006; 14 Bouhlel, Bartoli, Morlier, Otsmane (bib0005) 2016 Hwang, Martins (bib0019) 2018; 44 Kenway, Martins (bib0027) 2014; 51 Cheng, Younis, Hajikolaei, Wang (bib0037) 2015; 137 Deb (bib0040) 1998; 186 Gorissen, Crombecq, Couckuyt, Dhaene, Demeester (bib0002) 2010; 11 Forrester, Sobester, Keane (bib0035) 2008 Hwang, Martins (bib0007) 2018; 75 Powell (10.1016/j.advengsoft.2019.03.005_bib0010) 1992 Gray (10.1016/j.advengsoft.2019.03.005_bib0016) 2019 Martins (10.1016/j.advengsoft.2019.03.005_sbref0027) 2016; Green Aviation 10.1016/j.advengsoft.2019.03.005_bib0032 Gorissen (10.1016/j.advengsoft.2019.03.005_bib0002) 2010; 11 Hwang (10.1016/j.advengsoft.2019.03.005_bib0034) 2018 Kenway (10.1016/j.advengsoft.2019.03.005_bib0027) 2014; 51 Sacks (10.1016/j.advengsoft.2019.03.005_bib0009) 1989; 31 Deb (10.1016/j.advengsoft.2019.03.005_bib0040) 1998; 186 Mader (10.1016/j.advengsoft.2019.03.005_bib0024) 2008; 46 Bouhlel (10.1016/j.advengsoft.2019.03.005_sbref0005) 2016 Han (10.1016/j.advengsoft.2019.03.005_bib0021) 2013; 25 Jin (10.1016/j.advengsoft.2019.03.005_bib0008) 2005; 134 Martins (10.1016/j.advengsoft.2019.03.005_bib0018) 2013; 51 Lambe (10.1016/j.advengsoft.2019.03.005_bib0031) 2012; 46 Pedregosa (10.1016/j.advengsoft.2019.03.005_bib0001) 2011; 12 Hwang (10.1016/j.advengsoft.2019.03.005_bib0007) 2018; 75 Bartoli (10.1016/j.advengsoft.2019.03.005_bib0015) 2019 Forrester (10.1016/j.advengsoft.2019.03.005_sbref0033) 2008 Bouhlel (10.1016/j.advengsoft.2019.03.005_bib0004) 2016; 53 Kenway (10.1016/j.advengsoft.2019.03.005_bib0026) 2016; 54 Bouhlel (10.1016/j.advengsoft.2019.03.005_bib0006) 2019; 1 Liping (10.1016/j.advengsoft.2019.03.005_bib0038) 2006 Cheng (10.1016/j.advengsoft.2019.03.005_bib0037) 2015; 137 Hastie (10.1016/j.advengsoft.2019.03.005_bib0012) 2001 Rasmussen (10.1016/j.advengsoft.2019.03.005_bib0003) 2006 10.1016/j.advengsoft.2019.03.005_bib0025 Burdette (10.1016/j.advengsoft.2019.03.005_bib0029) 2019; 56 Morris (10.1016/j.advengsoft.2019.03.005_bib0039) 1993; 35 Shepard (10.1016/j.advengsoft.2019.03.005_bib0011) 1968 Bouhlel (10.1016/j.advengsoft.2019.03.005_bib0033) 2018; 50 Le Gratiet (10.1016/j.advengsoft.2019.03.005_sbref0022) 2013 Hwang (10.1016/j.advengsoft.2019.03.005_bib0030) 2019 Shang (10.1016/j.advengsoft.2019.03.005_bib0036) 2006; 14 Powell (10.1016/j.advengsoft.2019.03.005_sbref0013) 1994 Li (10.1016/j.advengsoft.2019.03.005_bib0014) 2019; 57 An (10.1016/j.advengsoft.2019.03.005_bib0023) 2001; 17 Martins (10.1016/j.advengsoft.2019.03.005_bib0017) 2013; 51 Hwang (10.1016/j.advengsoft.2019.03.005_bib0019) 2018; 44 Bettebghor (10.1016/j.advengsoft.2019.03.005_bib0020) 2011; 43
References_xml	– volume: 35 start-page: 243 year: 1993 end-page: 255 ident: bib0039 article-title: Bayesian design and analysis of computer experiments: use of derivatives in surface prediction publication-title: Technometrics – volume: 46 start-page: 863 year: 2008 end-page: 873 ident: bib0024 article-title: ADjoint: an approach for the rapid development of discrete adjoint solvers publication-title: AIAA J – reference: Lyu Z, Kenway GK, Paige C, Martins JRRA. Automatic differentiation adjoint of the Reynolds-averaged Navier–Stokes equations with a turbulence model. In: Proceedings of the twenty-first AIAA computational fluid dynamics conference. San Diego, CA; doi: – year: 2001 ident: bib0012 publication-title: The elements of statistical learning – volume: 51 start-page: 2049 year: 2013 end-page: 2075 ident: bib0017 article-title: Multidisciplinary design optimization: a survey of architectures publication-title: AIAA J – volume: 1 start-page: 157 year: 2019 end-page: 173 ident: bib0006 article-title: Gradient-enhanced kriging for high-dimensional problems publication-title: Eng Comput – volume: 25 start-page: 177 year: 2013 end-page: 189 ident: bib0021 article-title: Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function publication-title: Aerosp Sci Technol – year: 2016 ident: bib0005 article-title: An improved approach for estimating the hyperparameters of the kriging model for high-dimensional problems through the partial least squares method publication-title: Math Probl Eng – year: 2019 ident: bib0030 article-title: High-fidelity design-allocation optimization of a commercial aircraft maximizing airline profit publication-title: J Aircr – reference: Noesis Solutions. OPTIMUS. 2009. – volume: 44 start-page: Article37 year: 2018 ident: bib0019 article-title: A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives publication-title: ACM Trans Math Softw – volume: 46 start-page: 273 year: 2012 end-page: 284 ident: bib0031 article-title: Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes publication-title: Struct Multidiscip Optim – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bib0001 article-title: Scikit-learn: machine learning in python publication-title: J Mach Learn Res – volume: 137 start-page: 021407 year: 2015 ident: bib0037 article-title: Trust region based mode pursuing sampling method for global optimization of high dimensional design problems publication-title: J Mech Des – year: 2019 ident: bib0016 article-title: OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization publication-title: Structural and Multidisciplinary Optimization – volume: 56 start-page: 369 year: 2019 end-page: 384 ident: bib0029 article-title: Impact of morphing trailing edge on mission performance for the common research model publication-title: J Aircr – volume: Green Aviation start-page: 75 year: 2016 end-page: 79 ident: bib0028 article-title: Fuel burn reduction through wing morphing publication-title: Encyclopedia of aerospace engineering – volume: 14 start-page: 119 year: 2006 end-page: 126 ident: bib0036 article-title: A note on the extended rOsenbrock function publication-title: Evol Compuat – year: 2006 ident: bib0003 publication-title: Gaussian processes for machine learning – volume: 57 start-page: 581 year: 2019 end-page: 596 ident: bib0014 article-title: Data-based approach for fast airfoil analysis and optimization publication-title: J Aircr – year: 2018 ident: bib0034 article-title: Large-scale multidisciplinary optimization of an electric aircraft for on-demand mobility publication-title: Proceedings of the 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Kissimmee, FL – start-page: 51 year: 1994 end-page: 67 ident: bib0013 – volume: 186 start-page: 311 year: 1998 end-page: 338 ident: bib0040 article-title: An efficient constraint handling method for genetic algorithms publication-title: Comput Methods Appl Mech Eng – volume: 134 start-page: 268 year: 2005 end-page: 287 ident: bib0008 article-title: An efficient algorithm for constructing optimal design of computer experiments publication-title: J Stat Plan Inference – year: 2006 ident: bib0038 article-title: A comparison of metamodeling methods using practical industry requirements publication-title: Proceedings of the 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Newport, RI – volume: 43 start-page: 243 year: 2011 end-page: 259 ident: bib0020 article-title: Surrogate modeling approximation using a mixture of experts based on em joint estimation publication-title: Struct Multidiscip Optim – volume: 51 start-page: 2582 year: 2013 end-page: 2599 ident: bib0018 article-title: Review and unification of methods for computing derivatives of multidisciplinary computational models publication-title: AIAA J – volume: 31 start-page: 41 year: 1989 end-page: 47 ident: bib0009 article-title: Designs for computer experiments publication-title: Technometrics – volume: 11 start-page: 2051 year: 2010 end-page: 2055 ident: bib0002 article-title: A surrogate modeling and adaptive sampling toolbox for computer based design publication-title: J Mach Learn Res – start-page: 517 year: 1968 end-page: 524 ident: bib0011 article-title: A two-dimensional interpolation function for irregularly-spaced data publication-title: Proceedings of the twenty-third ACM national conference, ACM ’68 – year: 2013 ident: bib0022 publication-title: Multi-fidelity gaussian process regression for computer experiments – volume: 53 start-page: 935 year: 2016 end-page: 952 ident: bib0004 article-title: Improving kriging surrogates of high-dimensional design models by partial least squares dimension reduction publication-title: Struct Multidiscip Optim – reference: . – volume: 54 start-page: 113 year: 2016 end-page: 128 ident: bib0026 article-title: Multipoint aerodynamic shape optimization investigations of the common research model wing publication-title: AIAA J – start-page: 105 year: 1992 end-page: 210 ident: bib0010 publication-title: The theory of radial basis function approximation in 1990 – volume: 17 start-page: 588 year: 2001 end-page: 607 ident: bib0023 article-title: Quasi-regression publication-title: J Complex – volume: 51 start-page: 144 year: 2014 end-page: 160 ident: bib0027 article-title: Multipoint high-fidelity aerostructural optimization of a transport aircraft configuration publication-title: J Aircr – year: 2008 ident: bib0035 publication-title: Engineering design via surrogate modelling-A practical guide. – volume: 50 start-page: 2038 year: 2018 end-page: 2053 ident: bib0033 article-title: Efficient global optimization for high-dimensional constrained problems by using the kriging models combined with the partial least squares method publication-title: Eng Optim – volume: 75 start-page: 74 year: 2018 end-page: 87 ident: bib0007 article-title: A fast-prediction surrogate model for large datasets publication-title: Aerosp Sci Technol – year: 2019 ident: bib0015 article-title: Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design publication-title: Aerosp Sci Technol – year: 2006 ident: 10.1016/j.advengsoft.2019.03.005_bib0003 – volume: 75 start-page: 74 year: 2018 ident: 10.1016/j.advengsoft.2019.03.005_bib0007 article-title: A fast-prediction surrogate model for large datasets publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2017.12.030 – volume: 51 start-page: 144 issue: 1 year: 2014 ident: 10.1016/j.advengsoft.2019.03.005_bib0027 article-title: Multipoint high-fidelity aerostructural optimization of a transport aircraft configuration publication-title: J Aircr doi: 10.2514/1.C032150 – volume: 51 start-page: 2049 issue: 9 year: 2013 ident: 10.1016/j.advengsoft.2019.03.005_bib0017 article-title: Multidisciplinary design optimization: a survey of architectures publication-title: AIAA J doi: 10.2514/1.J051895 – year: 2006 ident: 10.1016/j.advengsoft.2019.03.005_bib0038 article-title: A comparison of metamodeling methods using practical industry requirements – volume: Green Aviation start-page: 75 year: 2016 ident: 10.1016/j.advengsoft.2019.03.005_sbref0027 article-title: Fuel burn reduction through wing morphing – year: 2008 ident: 10.1016/j.advengsoft.2019.03.005_sbref0033 – start-page: 517 year: 1968 ident: 10.1016/j.advengsoft.2019.03.005_bib0011 article-title: A two-dimensional interpolation function for irregularly-spaced data – year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0016 article-title: OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization publication-title: Structural and Multidisciplinary Optimization doi: 10.1007/s00158-019-02211-z – year: 2018 ident: 10.1016/j.advengsoft.2019.03.005_bib0034 article-title: Large-scale multidisciplinary optimization of an electric aircraft for on-demand mobility – volume: 56 start-page: 369 issue: 1 year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0029 article-title: Impact of morphing trailing edge on mission performance for the common research model publication-title: J Aircr doi: 10.2514/1.C034967 – volume: 186 start-page: 311 issue: 2–4 year: 1998 ident: 10.1016/j.advengsoft.2019.03.005_bib0040 article-title: An efficient constraint handling method for genetic algorithms publication-title: Comput Methods Appl Mech Eng – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.advengsoft.2019.03.005_bib0001 article-title: Scikit-learn: machine learning in python publication-title: J Mach Learn Res – volume: 17 start-page: 588 issue: 4 year: 2001 ident: 10.1016/j.advengsoft.2019.03.005_bib0023 article-title: Quasi-regression publication-title: J Complex doi: 10.1006/jcom.2001.0588 – volume: 137 start-page: 021407 issue: 2 year: 2015 ident: 10.1016/j.advengsoft.2019.03.005_bib0037 article-title: Trust region based mode pursuing sampling method for global optimization of high dimensional design problems publication-title: J Mech Des doi: 10.1115/1.4029219 – volume: 50 start-page: 2038 issue: 12 year: 2018 ident: 10.1016/j.advengsoft.2019.03.005_bib0033 article-title: Efficient global optimization for high-dimensional constrained problems by using the kriging models combined with the partial least squares method publication-title: Eng Optim doi: 10.1080/0305215X.2017.1419344 – volume: 11 start-page: 2051 year: 2010 ident: 10.1016/j.advengsoft.2019.03.005_bib0002 article-title: A surrogate modeling and adaptive sampling toolbox for computer based design publication-title: J Mach Learn Res – volume: 1 start-page: 157 issue: 35 year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0006 article-title: Gradient-enhanced kriging for high-dimensional problems publication-title: Eng Comput doi: 10.1007/s00366-018-0590-x – year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0015 article-title: Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2019.03.041 – ident: 10.1016/j.advengsoft.2019.03.005_bib0025 doi: 10.2514/6.2013-2581 – year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0030 article-title: High-fidelity design-allocation optimization of a commercial aircraft maximizing airline profit publication-title: J Aircr doi: 10.2514/1.C035082 – volume: 53 start-page: 935 issue: 5 year: 2016 ident: 10.1016/j.advengsoft.2019.03.005_bib0004 article-title: Improving kriging surrogates of high-dimensional design models by partial least squares dimension reduction publication-title: Struct Multidiscip Optim doi: 10.1007/s00158-015-1395-9 – year: 2001 ident: 10.1016/j.advengsoft.2019.03.005_bib0012 – volume: 43 start-page: 243 issue: 2 year: 2011 ident: 10.1016/j.advengsoft.2019.03.005_bib0020 article-title: Surrogate modeling approximation using a mixture of experts based on em joint estimation publication-title: Struct Multidiscip Optim doi: 10.1007/s00158-010-0554-2 – volume: 35 start-page: 243 issue: 3 year: 1993 ident: 10.1016/j.advengsoft.2019.03.005_bib0039 article-title: Bayesian design and analysis of computer experiments: use of derivatives in surface prediction publication-title: Technometrics doi: 10.1080/00401706.1993.10485320 – volume: 44 start-page: Article37 issue: 4 year: 2018 ident: 10.1016/j.advengsoft.2019.03.005_bib0019 article-title: A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives publication-title: ACM Trans Math Softw doi: 10.1145/3182393 – volume: 134 start-page: 268 issue: 1 year: 2005 ident: 10.1016/j.advengsoft.2019.03.005_bib0008 article-title: An efficient algorithm for constructing optimal design of computer experiments publication-title: J Stat Plan Inference doi: 10.1016/j.jspi.2004.02.014 – volume: 46 start-page: 273 year: 2012 ident: 10.1016/j.advengsoft.2019.03.005_bib0031 article-title: Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes publication-title: Struct Multidiscip Optim doi: 10.1007/s00158-012-0763-y – volume: 31 start-page: 41 issue: 1 year: 1989 ident: 10.1016/j.advengsoft.2019.03.005_bib0009 article-title: Designs for computer experiments publication-title: Technometrics doi: 10.1080/00401706.1989.10488474 – volume: 57 start-page: 581 issue: 2 year: 2019 ident: 10.1016/j.advengsoft.2019.03.005_bib0014 article-title: Data-based approach for fast airfoil analysis and optimization publication-title: J Aircr – year: 2013 ident: 10.1016/j.advengsoft.2019.03.005_sbref0022 – start-page: 105 year: 1992 ident: 10.1016/j.advengsoft.2019.03.005_bib0010 – year: 2016 ident: 10.1016/j.advengsoft.2019.03.005_sbref0005 article-title: An improved approach for estimating the hyperparameters of the kriging model for high-dimensional problems through the partial least squares method publication-title: Math Probl Eng doi: 10.1155/2016/6723410 – volume: 46 start-page: 863 issue: 4 year: 2008 ident: 10.1016/j.advengsoft.2019.03.005_bib0024 article-title: ADjoint: an approach for the rapid development of discrete adjoint solvers publication-title: AIAA J doi: 10.2514/1.29123 – volume: 54 start-page: 113 issue: 1 year: 2016 ident: 10.1016/j.advengsoft.2019.03.005_bib0026 article-title: Multipoint aerodynamic shape optimization investigations of the common research model wing publication-title: AIAA J doi: 10.2514/1.J054154 – start-page: 51 year: 1994 ident: 10.1016/j.advengsoft.2019.03.005_sbref0013 – volume: 51 start-page: 2582 issue: 11 year: 2013 ident: 10.1016/j.advengsoft.2019.03.005_bib0018 article-title: Review and unification of methods for computing derivatives of multidisciplinary computational models publication-title: AIAA J doi: 10.2514/1.J052184 – volume: 25 start-page: 177 issue: 1 year: 2013 ident: 10.1016/j.advengsoft.2019.03.005_bib0021 article-title: Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2012.01.006 – ident: 10.1016/j.advengsoft.2019.03.005_bib0032 – volume: 14 start-page: 119 issue: 1 year: 2006 ident: 10.1016/j.advengsoft.2019.03.005_bib0036 article-title: A note on the extended rOsenbrock function publication-title: Evol Compuat doi: 10.1162/evco.2006.14.1.119
SSID	ssj0014021
Score	2.6448843
Snippet	The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and...
SourceID	hal crossref elsevier
SourceType	Open Access Repository Enrichment Source Index Database Publisher
StartPage	102662
SubjectTerms	Computer Science Derivatives Engineering Sciences Gradient-enhanced surrogate modeling Mathematics Physics Surrogate modeling
Title	A Python surrogate modeling framework with derivatives
URI	https://dx.doi.org/10.1016/j.advengsoft.2019.03.005 https://hal.science/hal-02294310
Volume	135
WOSCitedRecordID	wos000502030600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0965-9978 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0014021 providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELZKlwMceCwglpcixK3Kqknq2BangBYVtFQrVKTeosQP2lU3WSVtdznzxxnHsRPxEAWJSxRZsWN3ppNvxp9nEHolQ0LGmAc-CyUGByUK_ZwyCndUYU6UyGXeFJsgsxldLNjZYPDNnoXZrUlR0OtrdvlfRQ1tIGx9dPYvxO0GhQa4B6HDFcQO170En4zOvuqEAKN6W1WljpKZcjcNZdJSsUz8VcB8dk3m77oPUhPDC2iYsrLLVziqwWRfaaaYTu7EWNiLIrwpt8t2w_9juYSXiFFy0duyn15lHfV3ND_uAqjVpjQntGc6ir9euS6nmcpM3fdPZjf_YtUPUQQdB8vFGmPsM2Zq9TizG-Ge4QScExuz_JNNN-GF8-NMgPn_opeqGXltblrcfcfs3v0PnzdHOrR8tvO0GynVI6XjKG3y4B6EBDM6RAfJ-5PFB7cZBS52U3jRrqIlhBma4K9n9TuUc2Np4_UNfpnfQ3dax8NLjMLcRwNZHKK7rRPitSa-hiZb58O2HaLbvaSVD1CceEbBPKdgnlUwzymYpxXM6ynYQ_T53cn87dRvq2_4HBa88YkE6ArwmWYkG4tAUqFUIABexnkQ84miWagw45STyYTLkOUYoHYsleJCBCrk0SM0LMpCPkYe42EOnnIU5SyHh6OcAHYSgRBEEKo4PkLE_lopb1PT6wop6_RPMjtCget5adKz7NHntRVI2sJMAx9T0Lg9er8EGbqX6ezs0-Q01W0Ahxng8fEuePIP03qKbnX_nWdouKm28jm6yXebVV29aHXyO6pPr2E
linkProvider	Elsevier
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+Python+surrogate+modeling+framework+with+derivatives&rft.jtitle=Advances+in+engineering+software+%281992%29&rft.au=Bouhlel%2C+Mohamed+Amine&rft.au=Hwang%2C+John+T.&rft.au=Bartoli%2C+Nathalie&rft.au=Lafage%2C+R%C3%A9mi&rft.date=2019-09-01&rft.issn=0965-9978&rft.volume=135&rft.spage=102662&rft_id=info:doi/10.1016%2Fj.advengsoft.2019.03.005&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_advengsoft_2019_03_005
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0965-9978&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0965-9978&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0965-9978&client=summon