The Surrogate Modeling Toolbox (SMT)
Project description
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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| Filename, size | File type | Python version | Upload date | Hashes |
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| Filename, size smt-0.4.1-cp27-cp27m-win_amd64.whl (225.5 kB) | File type Wheel | Python version cp27 | Upload date | Hashes View hashes |
| Filename, size smt-0.4.1-cp37-cp37m-win_amd64.whl (216.5 kB) | File type Wheel | Python version cp37 | Upload date | Hashes View hashes |
| Filename, size smt-0.4.1.tar.gz (252.4 kB) | File type Source | Python version None | Upload date | Hashes View hashes |
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