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The Surrogate Modeling Toolbox (SMT)

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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.

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Provenance

The following attestation bundles were made for smt-2.10.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build_release.yml on SMTorg/smt

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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Details for the file smt-2.10.1-cp310-cp310-macosx_11_0_arm64.whl.

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Hashes for smt-2.10.1-cp310-cp310-macosx_11_0_arm64.whl
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Provenance

The following attestation bundles were made for smt-2.10.1-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: build_release.yml on SMTorg/smt

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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