Metadata-Version: 2.1
Name: smt
Version: 0.3.4
Summary: The Surrogate Modeling Toolbox (SMT)
Home-page: https://github.com/SMTorg/smt
Author: Mohamed Amine Bouhlel et al.
Author-email: mbouhlel@umich.edu
License: BSD-3
Download-URL: https://github.com/SMTorg/smt/releases
Platform: UNKNOWN
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 :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*
Requires-Dist: scikit-learn
Requires-Dist: pyDOE2
Requires-Dist: matplotlib
Requires-Dist: numpydoc
Requires-Dist: six (>=1.10)
Requires-Dist: scipy

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.


