DACE, Design and Analysis of Computer Experiments, is a Matlab toolbox for working with kriging approximations to computer models. Typical use of this software is to construct a kriging approximation model based on data from a computer experiment, and to use this approximation model as a surrogate for the computer model. The software also addresses the design of experiment problem, that is choosing the inputs at which to evaluate the computer model for constructing the kriging approximation.

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  1. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  2. Granados-Ortiz, Francisco-Javier; Pérez Arroyo, Carlos; Puigt, Guillaume; Lai, Choi-Hong; Airiau, Christophe: On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust (2019)
  3. Morse, Llewellyn; Sharif Khodaei, Zahra; Aliabadi, M. H.: A multi-fidelity boundary element method for structural reliability analysis with higher-order sensitivities (2019)
  4. Yang, Zan; Qiu, Haobo; Gao, Liang; Jiang, Chen; Zhang, Jinhao: Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems (2019)
  5. Bertram, Anna; Zimmermann, Ralf: Theoretical investigations of the new cokriging method for variable-fidelity surrogate modeling. Well-posedness and maximum likelihood training. (2018)
  6. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  7. Nedělková, Zuzana; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith: Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space (2018)
  8. Wei, Pengfei; Liu, Fuchao; Lu, Zhenzhou; Wang, Zuotao: A probabilistic procedure for quantifying the relative importance of model inputs characterized by second-order probability models (2018)
  9. Xiao, Manyu; Zhang, Guohua; Breitkopf, Piotr; Villon, Pierre; Zhang, Weihong: Extended co-Kriging interpolation method based on multi-fidelity data (2018)
  10. Barbillon, Pierre; Barthélémy, Célia; Samson, Adeline: Parameter estimation of complex mixed models based on meta-model approach (2017)
  11. Jie, Haoxiang; Wu, Yizhong; Zhao, Jianjun; Ding, Jianwan; Liangliang: An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems (2017)
  12. Kleijnen, Jack P. C.: Regression and Kriging metamodels with their experimental designs in simulation: a review (2017)
  13. Li, Yaohui; Wu, Yizhong; Zhao, Jianjun; Chen, Liping: A kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points (2017)
  14. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
  15. Su, Hua; Gong, Chunlin; Gu, Liangxian: Kernel parameter optimization for kriging based on structural risk minimization principle (2017)
  16. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Pseudo expected improvement criterion for parallel EGO algorithm (2017)
  17. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Balancing global and local search in parallel efficient global optimization algorithms (2017)
  18. Zidek, Robert A. E.; Kolmanovsky, Ilya V.: Drift counteraction optimal control for deterministic systems and enhancing convergence of value iteration (2017)
  19. Balesdent, Mathieu; Morio, Jérôme; Brevault, Loïc: Rare event probability estimation in the presence of epistemic uncertainty on input probability distribution parameters (2016)
  20. Beyhaghi, Pooriya; Cavaglieri, Daniele; Bewley, Thomas: Delaunay-based derivative-free optimization via global surrogates. I: Linear constraints (2016)

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