First Public Release of the Multivariate Metamodeling Toolbox
Dirk Gorissen
Metamodeling Toolbox (M3-Toolbox). This toolbox provides a variety of
pluggable techniques for adaptively building scalable surrogate models
(neural networks, SVMs, rational functions,...).
The toolbox is free for non-commercial, personal, academic use. Feel free
to test, use, tune, extend and comment.
More information + downloads: http://www.coms.ua.ac.be/?q=m3_toolbox
Background
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In 2004, research within the COMS group was focused on developing efficient,
adaptive and accurate algorithms for polynomial and rational modeling of
linear time-invariant (LTI) systems. This work resulted in a set of Matlab
scripts that were used as a testing ground for new ideas and techniques.
Research progressed, and with time these scripts were re-worked and
refactored into one coherent Matlab toolbox, tentatively named the
Multivariate MetaModeling (M3) Toolbox.
Design Goals
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During research into multivariate metamodeling techniques and algorithms it
became clear that there was room for an adaptive tool that integrated
different surrogate modeling approaches and did not tie the user down to
one particular set of problems or techniques. More concretely, we were
unable to find evidence of any projects that integrated:
1. Building standalone global metamodels
2. Support for different model types, different modeling algorithms, ...
(adaptive modeling)
3. Sequential design
4. Distributed computing
5. Usable implementation in software
This gave rise to a number of design goals that served as the guidelines for
the design of the M3-toolbox. These goals are:
1. Development of a fully automated, adaptive metamodel construction
algorithm. Given a simulation model, the software should produce a
metamodel with as little user interaction as possible ("one button
approach").
2. There is no such thing as a "one-size-fits-all", different problems
need to be modeled differently and require different levels of process
knowledge. Therefore the software should be modular and extensible but not
be too cumbersome to use or configure (sensible defaults).
3. The toolbox should minimize the required prior knowledge of the system
to be modeled.
4. The algorithm should minimize the number of required samples in order
to come to an acceptable metamodel.
5. The algorithm should terminate only when the predefined accuracy (set
by the user) has been reached or the maximum number of iterations has been
exceeded.
License Terms
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The M3-Toolbox is available for private, personal, NON-COMMERCIAL use only
Download
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Please refer to: http://www.coms.ua.ac.be/?q=m3_toolbox