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. Barbillon, Pierre; Barthélémy, Célia; Samson, Adeline: Parameter estimation of complex mixed models based on meta-model approach (2017)
  2. 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)
  3. 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)
  4. 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)
  5. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Pseudo expected improvement criterion for parallel EGO algorithm (2017)
  6. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Balancing global and local search in parallel efficient global optimization algorithms (2017)
  7. Zidek, Robert A.E.; Kolmanovsky, Ilya V.: Drift counteraction optimal control for deterministic systems and enhancing convergence of value iteration (2017)
  8. Balesdent, Mathieu; Morio, Jér^ome; Brevault, Loïc: Rare event probability estimation in the presence of epistemic uncertainty on input probability distribution parameters (2016)
  9. Beyhaghi, Pooriya; Cavaglieri, Daniele; Bewley, Thomas: Delaunay-based derivative-free optimization via global surrogates. I: Linear constraints (2016)
  10. Jiang, Ting; Zhou, Xiaojian: Estimation of actuarial quantities at fractional ages with Kriging (2016)
  11. Li, Gang; Meng, Zeng; Hao, Peng; Hu, Hao: A hybrid reliability-based design optimization approach with adaptive chaos control using Kriging model (2016)
  12. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  13. Mohammad Zadeh, Parviz; Mehmani, Ali; Messac, Achille: High fidelity multidisciplinary design optimization of a wing using the interaction of low and high fidelity models (2016)
  14. Nedělková, Zuzana; Lindroth, Peter; Strömberg, Ann-Brith; Patriksson, Michael: Integration of expert knowledge into radial basis function surrogate models (2016)
  15. Nguyen, Nhung; Shao, Yue; Wineman, Alan; Fu, Jianping; Waas, Anthony: Atomic force microscopy indentation and inverse analysis for non-linear viscoelastic identification of breast cancer cells (2016)
  16. Wurm, Andreas; Bestle, Dieter: Robust design optimization for improving automotive shift quality (2016)
  17. Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe: A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry (2015)
  18. Sen, Oishik; Davis, Sean; Jacobs, Gustaaf; Udaykumar, H.S.: Evaluation of convergence behavior of metamodeling techniques for bridging scales in multi-scale multimaterial simulation (2015)
  19. Yang, Qinwen; Xue, Deyi: A weighted sequential sampling method considering influences of sample qualities in input and output parameter spaces for global optimization (2015)
  20. Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)

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