Metadata-Version: 2.4
Name: smt
Version: 2.9.2
Summary: The Surrogate Modeling Toolbox (SMT)
Home-page: https://github.com/SMTorg/smt
Download-URL: https://github.com/SMTorg/smt/releases
Author: Remi Lafage et al.
Author-email: remi.lafage@onera.fr
License: BSD-3
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.9
License-File: LICENSE.txt
Requires-Dist: packaging
Requires-Dist: scikit-learn
Requires-Dist: pyDOE3
Requires-Dist: scipy
Requires-Dist: jenn
Provides-Extra: numba
Requires-Dist: numba~=0.56.4; extra == "numba"
Provides-Extra: gpx
Requires-Dist: egobox~=0.23; extra == "gpx"
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Dynamic: classifier
Dynamic: description
Dynamic: download-url
Dynamic: home-page
Dynamic: license
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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.
