SMT: Surrogate Modeling Toolbox

The surrogate modeling toolbox (SMT) is an open-source Python package consisting of libraries of surrogate modeling methods (e.g., radial basis functions, kriging), sampling methods, and benchmarking problems. SMT is designed to make it easy for developers to implement new surrogate models in a well-tested and well-document platform, and for users to have a library of surrogate modeling methods with which to use and compare methods.

The code is available open-source on GitHub together with introducing tutorials.

Other toolboxes linked to SMT are available:

  • smt-optim for multi-fidelity and multi-objective Bayesian optimization with or without constraint

  • smt-design-space-ext to handle hierarchical variables in the design space

  • smt-explainability to provide a collection of model-agnostic explainable AI methods

SMT is developped and maintained by contributors worldwide and supported by research institutions.

Cite us

To cite SMT 2.0: P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. H. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins.

SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes, Advances in Engineering Software, 2024.

@article{saves2024smt,
        author = {P. Saves and R. Lafage and N. Bartoli and Y. Diouane and J. Bussemaker and T. Lefebvre and J. T. Hwang and J. Morlier and J. R. R. A. Martins},
        title = {{SMT 2.0: A} Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes},
        journal = {Advances in Engineering Sofware},
        year = {2024},
        volume = {188},
        pages = {103571},
        doi = {https://doi.org/10.1016/j.advengsoft.2023.103571}}

To cite SMT legacy: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins.

A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.

@article{SMT2019,
        Author = {Mohamed Amine Bouhlel and John T. Hwang and Nathalie Bartoli and Rémi Lafage and Joseph Morlier and Joaquim R. R. A. Martins},
        Journal = {Advances in Engineering Software},
        Title = {A Python surrogate modeling framework with derivatives},
        pages = {102662},
        issn = {0965-9978},
        doi = {https://doi.org/10.1016/j.advengsoft.2019.03.005},
        Year = {2019}}

Documentation contents

Indices and tables