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.
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