A Python surrogate modeling framework with derivatives. (September 2019)

• Record Type: Journal Article
• Authors: Bouhlel, Mohamed Amine; Hwang, John T.; Bartoli, Nathalie; Lafage, Rémi; Morlier, Joseph; Martins, Joaquim R.R.A.
• Is Part Of: Advances in engineering software. Volume 135(2019)
• Publication Date: 2019-09
• Subjects: Surrogate modeling -- Gradient-enhanced surrogate modeling -- Derivatives; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2019.03.005
• Languages: English
• ISSNs: 0965-9978

Abstract

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. 1