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A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments

Joachim van der Herten (UGent) , Ivo Couckuyt (UGent) , Dirk Deschrijver (UGent) and Tom Dhaene (UGent)
(2015) SIAM JOURNAL ON SCIENTIFIC COMPUTING. 37(2). p.A1020-A1039
Author
Organization
Project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
Abstract
Complex real-world systems can accurately be modeled by simulations. Evaluating high-fidelity simulators can take several days, making them impractical for use in optimization, design space exploration, and analysis. Often, these simulators are approximated by relatively simple math known as a surrogate model. The data points to construct this model are simulator evaluations meaning the choice of these points is crucial: each additional data point can be very expensive in terms of computing time. Sequential design strategies offer a huge advantage over one-shot experimental design because information gathered from previous data points can be used in the process of determining new data points. Previously, LOLA-Voronoi was presented as a hybrid sequential design method which balances exploration and exploitation: the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in interesting regions which were previously discovered. Although this approach is very successful in terms of the required number of data points to build an accurate surrogate model, it is computationally intensive. This paper presents a new approach to the exploitation component of the algorithm based on fuzzy logic. The new approach has the same desirable properties as the old method but is less complex, especially when applied to high-dimensional problems. Experiments on several test problems show the new approach is a lot faster, without losing robustness or requiring additional samples to obtain similar model accuracy.
Keywords
SENSITIVITY, IBCN, PERFORMANCE, ALGORITHM, sequential design, active learning, high-dimensional, experimental design, fuzzy inference systems

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Citation

Please use this url to cite or link to this publication:

MLA
van der Herten, Joachim et al. “A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-dimensional Computer Experiments.” SIAM JOURNAL ON SCIENTIFIC COMPUTING 37.2 (2015): A1020–A1039. Print.
APA
van der Herten, J., Couckuyt, I., Deschrijver, D., & Dhaene, T. (2015). A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 37(2), A1020–A1039.
Chicago author-date
van der Herten, Joachim, Ivo Couckuyt, Dirk Deschrijver, and Tom Dhaene. 2015. “A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-dimensional Computer Experiments.” Siam Journal on Scientific Computing 37 (2): A1020–A1039.
Chicago author-date (all authors)
van der Herten, Joachim, Ivo Couckuyt, Dirk Deschrijver, and Tom Dhaene. 2015. “A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-dimensional Computer Experiments.” Siam Journal on Scientific Computing 37 (2): A1020–A1039.
Vancouver
1.
van der Herten J, Couckuyt I, Deschrijver D, Dhaene T. A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments. SIAM JOURNAL ON SCIENTIFIC COMPUTING. 2015;37(2):A1020–A1039.
IEEE
[1]
J. van der Herten, I. Couckuyt, D. Deschrijver, and T. Dhaene, “A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments,” SIAM JOURNAL ON SCIENTIFIC COMPUTING, vol. 37, no. 2, pp. A1020–A1039, 2015.
@article{7025961,
  abstract     = {Complex real-world systems can accurately be modeled by simulations. Evaluating high-fidelity simulators can take several days, making them impractical for use in optimization, design space exploration, and analysis. Often, these simulators are approximated by relatively simple math known as a surrogate model. The data points to construct this model are simulator evaluations meaning the choice of these points is crucial: each additional data point can be very expensive in terms of computing time. Sequential design strategies offer a huge advantage over one-shot experimental design because information gathered from previous data points can be used in the process of determining new data points. Previously, LOLA-Voronoi was presented as a hybrid sequential design method which balances exploration and exploitation: the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in interesting regions which were previously discovered. Although this approach is very successful in terms of the required number of data points to build an accurate surrogate model, it is computationally intensive. This paper presents a new approach to the exploitation component of the algorithm based on fuzzy logic. The new approach has the same desirable properties as the old method but is less complex, especially when applied to high-dimensional problems. Experiments on several test problems show the new approach is a lot faster, without losing robustness or requiring additional samples to obtain similar model accuracy.},
  author       = {van der Herten, Joachim and Couckuyt, Ivo and Deschrijver, Dirk and Dhaene, Tom},
  issn         = {1064-8275},
  journal      = {SIAM JOURNAL ON SCIENTIFIC COMPUTING},
  keywords     = {SENSITIVITY,IBCN,PERFORMANCE,ALGORITHM,sequential design,active learning,high-dimensional,experimental design,fuzzy inference systems},
  language     = {eng},
  number       = {2},
  pages        = {A1020--A1039},
  title        = {A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments},
  url          = {http://dx.doi.org/10.1137/140962437},
  volume       = {37},
  year         = {2015},
}

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