SMTorg/smt
Surrogate Modeling Toolbox
When you need to build fast, lightweight mathematical models from complex data, the Surrogate Modeling Toolbox (SMT) helps you create simplified representations. It takes in your existing data points and outputs a 'surrogate model' that mimics the behavior of your original system without needing to run expensive simulations. This is ideal for engineers, scientists, and researchers who perform design optimization, uncertainty quantification, or simulation analysis.
861 stars. Actively maintained with 5 commits in the last 30 days. Available on PyPI.
Use this if you need to quickly estimate outputs or explore design spaces for computationally expensive simulations, especially when sensitivity to changes (derivatives) is important.
Not ideal if your problem requires interpreting results from simple statistical models or if you need to build complex machine learning models with large, varied datasets.
Stars
861
Forks
226
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Mar 12, 2026
Commits (30d)
5
Dependencies
5
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