The Surrogate Modeling Toolbox (SUMO Toolbox) is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (simulation code, data set, script, ...) within the accuracy and time constraints set by the user. In doing so the toolbox minimizes the number of data points (which it chooses automatically) since they are usually expensive. The toolbox tries to be as adaptive and autonomous as possible, requiring no user input besides some initial configuration.
However, since there is no such thing as a one-size-fits-all, the toolbox has been designed to be fully pluggable and extensible using standard object oriented design patterns. Implementations of the different components (model types, sampling strategies, model selection criteria, hyperparameter optimization algorithms,...) can be plugged-in, compared, or replaced by custom implementations. In this way the SUMO Toolbox provides a common platform to easily test and benchmark different sampling and approximation strategies while easily integrating in the engineering design process.
- Changes to previous version:
Incremental update, fixing some cosmetic issues, coincides with JMLR publication.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage
- Supported Operating Systems: Agnostic
- Data Formats: Ascii, Binary, Matlab, Java Arrays, Txt
- Tags: Regression, Active Learning, Machine Learning, Metamodeling, Model Selection, Simulation
- Archive: download here
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