smt


Namesmt JSON
Version 2.9.4 PyPI version JSON
download
home_pagehttps://github.com/SMTorg/smt
SummaryThe Surrogate Modeling Toolbox (SMT)
upload_time2025-08-19 12:33:35
maintainerNone
docs_urlNone
authorRemi Lafage et al.
requires_python>=3.9
licenseBSD-3
keywords
VCS
bugtrack_url
requirements Cython packaging numpy scipy scikit-learn pyDOE3 numba matplotlib pytest pytest-xdist pytest-cov ruff jenn egobox
Travis-CI No Travis.
coveralls test coverage
            
The surrogate modeling toolbox (SMT) is a 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 the training data. It also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation.

SMT 2.0 adds the capability to handle mixed-variable surrogate models and hierarchical variables.

            

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