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. 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)
  2. 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)
  3. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Pseudo expected improvement criterion for parallel EGO algorithm (2017)
  4. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Balancing global and local search in parallel efficient global optimization algorithms (2017)
  5. Balesdent, Mathieu; Morio, Jér^ome; Brevault, Loïc: Rare event probability estimation in the presence of epistemic uncertainty on input probability distribution parameters (2016)
  6. Beyhaghi, Pooriya; Cavaglieri, Daniele; Bewley, Thomas: Delaunay-based derivative-free optimization via global surrogates. I: Linear constraints (2016)
  7. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  8. 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)
  9. Nedělková, Zuzana; Lindroth, Peter; Strömberg, Ann-Brith; Patriksson, Michael: Integration of expert knowledge into radial basis function surrogate models (2016)
  10. 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)
  11. Wurm, Andreas; Bestle, Dieter: Robust design optimization for improving automotive shift quality (2016)
  12. 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)
  13. 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)
  14. Yang, Qinwen; Xue, Deyi: A weighted sequential sampling method considering influences of sample qualities in input and output parameter spaces for global optimization (2015)
  15. Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)
  16. Couckuyt, Ivo; Dhaene, Tom; Demeester, Piet: ooDACE toolbox: a flexible object-oriented Kriging implementation (2014)
  17. Kleijnen, Jack P.C.; Mehdad, Ehsan: Multivariate versus univariate Kriging metamodels for multi-response simulation models (2014)
  18. Long, C.C.; Marsden, A.L.; Bazilevs, Y.: Shape optimization of pulsatile ventricular assist devices using FSI to minimize thrombotic risk (2014)
  19. Müller, Juliane; Shoemaker, Christine A.: Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems (2014)
  20. Biron, Guillaume; Vadean, Aurelian; Tudose, Lucian: Optimal design of interference fit assemblies subjected to fatigue loads: a sequential approximate multi-objective optimization approach (2013)

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