A powerful object oriented kriging Matlab toolbox
The ooDACE Toolbox (Design and Analysis of Computer Experiments) is a powerful and versatile Matlab toolbox for building kriging surrogate models of a given data set (e.g., based on computationally expensive simulations or physical experiments). Kriging is, in particular, popular for approximating (and optimizing) deterministic computer experiments. Kriging surrogate models are compact scalable regression models which can be used efficiently for design automation, parametric studies, design space exploration, optimization, yield improvement, visualization, prototyping, and sensitivity analysis. The ooDACE Toolbox provides a flexible Object Oriented Matlab implementation, easily extendable and well-suited to test and benchmark new kriging flavors.
Matlab 2015a or newer is recommended.
Documentation | Download instructions | Features | Screenshots |References
Support for several variants of kriging: simple kriging, ordinary kriging, universal kriging, blind kriging, co-kriging,regression kriging, etc.
Efficient and flexible hyperparameter optimization
Can handle noisy data (by including an extra parameter in the hyperparameter optimization) = regression kriging
Interface compatible with the popular Matlab DACE toolbox
Proper Object Oriented (OO) design
The ooDACE Toolbox is available in 3 different forms:
Fully functional proprietary versions (1) for commercial use, and (2) for funded academic research.
Open Source version, only (3) for personal, non-profit, pure academic research and for educational purposes .
Details can be found in the License Terms.
Academics can download (3) the open source version from the menu on the left, for personal, non-profit, basic or pure academic research only. Some restrictions might apply.
Note that free unrestricted or flexible licensing schemes are available for research partners (aiming at collaboration, data exchange, or joint publications).
When reporting results obtained by the ooDACE Toolbox, please refer to:
- ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation I. Couckuyt, T. Dhaene, P. Demeester, Journal of Machine Learning Research, Vol. 15, pp. 3183-3186, October 2014.
- Blind Kriging: Implementation and performance analysis I. Couckuyt, A. Forrester, D. Gorissen, F. De Turck, T. Dhaene, Advances in Engineering Software (Elsevier), Vol. 49, No. 3, pp. 1-13, July 2012.
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