SUrrogate MOdeling (SUMO) Toolbox

SUMO Toolbox

 
 
from data to knowledge

Brief description

SUMO ToolboxThe 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 (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive. More information...


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Key features

  1. Efficiency
    • Fast design space exploration: compact scalable regression models for design automation, parametric studies, design space exploration, optimization, yield improvement, visualization, prototyping, and sensitivity analysis

    • Gain insight: knowledge discovery in sparse data sets, and knowledge extraction from large data sets

  2. Accuracy
    • Best-in-class modeling techniques: highly accurate and efficient proprietary and state-of-the-art surrogate modeling algorithms

  3. Ease-of-use
    • Expert know-how at your fingertips: sensible default settings, based on expert knowledge from various disciplines (e.g., machine learning, approximation theory, numerical analysis, statistics, optimization, ...), and also many expert options available

    • Powerful logging and profiling tools: intermediate models (and plots) stored for further reference, extensive logging of what is going on, profiling framework to track modeling progress

  4. Automation
    • Active learning: automatic selection of data points (also known as adaptive sample selection, sequential design, or optimal experimental design)

    • Model selection: automatic selection of model type (e.g., ANN, SVM, rational model, ...) and model complexity (e.g., number of neurons and hidden layers, kernel function, order, ...)

  5. Flexibility
    • Pluggable and extensible framework: easy integration of custom implementations (e.g., sampling strategies, model types, model selection criteria, hyperparameter optimization algorithms,...)

    • Flexible experimental environment: easy to setup and run different modeling experiments, easy to benchmark different techniques

    • Multi-platform: available for Windows, Mac OSX, and Unix/Linux platforms

  6. Speed
    • Shorten time to market: lower cost and shorten process cycle time

    • Distributed computing: integration with cluster and grid middleware to transparently run simulations in parallel

A set of overview slides is available here: SUMO_presentation.pdf

 

Download instructions

The SUMO Toolbox is available in 3 different forms:

  • Fully functional proprietary versions (1) for commercial use, and (2) for funded academic research.

  • Restricted academic version, only (3) for personal, non-profit, pure academic research and for educational purposes .

Details can be found in the License Terms.

 

For details about (1) a commercial license or (2) an academic license for applied industrial research research, please see the order form pdf and use the contact form

Academics can download (3) the free academic version from the menu on the left, only for personal, non-profit, basic or pure academic research and for educational purposes. Some restrictions apply.

 

Note that free unrestricted or flexible licensing schemes and technical support are also available for research partners (aiming at collaboration, data exchange, or joint publications).

 

Reference

When reporting results obtained by the SUMO Toolbox, please refer to:

  • A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design pdf D. Gorissen, K. Crombecq, I. Couckuyt, T. Dhaene, P. Demeester, Journal of Machine Learning Research, Vol. 11, pp. 2051-2055, July 2010.
  • Adaptive classification under computational budget constraints using sequential data gathering pdf J. van der Herten, I. Couckuyt, D. Deschrijver, T. Dhaene, Advances in Engineering Software, Vol. 99, pp. 137–146, September 2016.

 


Copyright notice The materials displayed on this website are protected by copyright and other intellectual property laws. All rights are retained by SUMO Lab - IBCN - UGent - iMINDS. If the code is used in a scientific work, or in a commercial program, the user is kindly requested to make reference to the code and the corresponding publication.

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