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. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  2. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  3. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems (2020)
  4. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  5. Zhou, Yicheng; Lu, Zhenzhou; Hu, Jinghan; Hu, Yingshi: Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (2020)
  6. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  7. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  8. Cortesi, Andrea F.; Jannoun, Ghina; Congedo, Pietro M.: Kriging-sparse polynomial dimensional decomposition surrogate model with adaptive refinement (2019)
  9. 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)
  10. Morse, Llewellyn; Sharif Khodaei, Zahra; Aliabadi, M. H.: A multi-fidelity boundary element method for structural reliability analysis with higher-order sensitivities (2019)
  11. Qian, Hua-Ming; Huang, Hong-Zhong; Li, Yan-Feng: A novel single-loop procedure for time-variant reliability analysis based on Kriging model (2019)
  12. Shi, Yan; Lu, Zhenzhou; Xu, Liyang; Chen, Siyu: An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis (2019)
  13. Tran, Anh; Sun, Jing; Furlan, John M.; Pagalthivarthi, Krishnan V.; Visintainer, Robert J.; Wang, Yan: pBO-2GP-3B: a batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics (2019)
  14. Yang, Zan; Qiu, Haobo; Gao, Liang; Jiang, Chen; Zhang, Jinhao: Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems (2019)
  15. Zhang, Jinhao; Xiao, Mi; Gao, Liang: A new method for reliability analysis of structures with mixed random and convex variables (2019)
  16. Bertram, Anna; Zimmermann, Ralf: Theoretical investigations of the new cokriging method for variable-fidelity surrogate modeling. Well-posedness and maximum likelihood training. (2018)
  17. Chatterjee, Tanmoy; Chowdhury, Rajib: (h)-(p) adaptive model based approximation of moment free sensitivity indices (2018)
  18. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  19. 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)
  20. Vahedi, Jafar; Ghasemi, Mohammad Reza; Miri, Mahmoud: An adaptive divergence-based method for structural reliability analysis via multiple Kriging models (2018)

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