Skip to main content

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smt-0.4.1.tar.gz (252.4 kB view hashes)

Uploaded Source

Built Distributions

smt-0.4.1-cp37-cp37m-win_amd64.whl (216.5 kB view hashes)

Uploaded CPython 3.7m Windows x86-64

smt-0.4.1-cp27-cp27m-win_amd64.whl (225.5 kB view hashes)

Uploaded CPython 2.7m Windows x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page