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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Veröffentlicht in:Advances in engineering software (1992) Jg. 135; S. 102662
Hauptverfasser: Bouhlel, Mohamed Amine, Hwang, John T., Bartoli, Nathalie, Lafage, Rémi, Morlier, Joseph, Martins, Joaquim R.R.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2019
Elsevier
Schlagworte:
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-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2019.03.005