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...

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Published in:	Advances in engineering software (1992) Vol. 135; p. 102662
Main Authors:	Bouhlel, Mohamed Amine, Hwang, John T., Bartoli, Nathalie, Lafage, Rémi, Morlier, Joseph, Martins, Joaquim R.R.A.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2019 Elsevier
Subjects:	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
Online Access:	Get full text
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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
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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
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Snippet	The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and...
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SubjectTerms	Computer Science Derivatives Engineering Sciences Gradient-enhanced surrogate modeling Mathematics Physics Surrogate modeling
Title	A Python surrogate modeling framework with derivatives
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