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. Cai, Xuefei; Kolomenskiy, Dmitry; Nakata, Toshiyuki; Liu, Hao: A CFD data-driven aerodynamic model for fast and precise prediction of flapping aerodynamics in various flight velocities (2021)
  2. Guo, Qing; Liu, Yongshou; Chen, Bingqian; Zhao, Yuzhen: An efficient stochastic natural frequency analysis method for axially varying functionally graded material pipe conveying fluid (2021)
  3. Hong, Linxiong; Li, Huacong; Gao, Ning; Fu, Jiangfeng; Peng, Kai: Random and multi-super-ellipsoidal variables hybrid reliability analysis based on a novel active learning Kriging model (2021)
  4. Jensen, H.; Jerez, D.; Beer, M.: A general two-phase Markov chain Monte Carlo approach for constrained design optimization: application to stochastic structural optimization (2021)
  5. Rathi, Amit Kumar; Chakraborty, Arunasis: Improved moving least square-based multiple dimension decomposition (MDD) technique for structural reliability analysis (2021)
  6. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  7. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  8. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems (2020)
  9. García-García, José Carlos; García-Ródenas, Ricardo; Codina, Esteve: A surrogate-based cooperative optimization framework for computationally expensive black-box problems (2020)
  10. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  11. Shi, Yan; Lu, Zhenzhou; Zhou, Jiayan; Zio, Enrico: A novel time-dependent system constraint boundary sampling technique for solving time-dependent reliability-based design optimization problems (2020)
  12. Tajbakhsh, Sam Davanloo; Aybat, Necdet Serhat; Del Castillo, Enrique: On the theoretical guarantees for parameter estimation of Gaussian random field models: a sparse precision matrix approach (2020)
  13. Tifkitsis, K. I.; Skordos, A. A.: Real-time inverse solution of the composites’ cure heat transfer problem under uncertainty (2020)
  14. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  15. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)
  16. Zafar, Tayyab; Zhang, Yanwei; Wang, Zhonglai: An efficient Kriging based method for time-dependent reliability based robust design optimization via evolutionary algorithm (2020)
  17. 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)
  18. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  19. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  20. Cortesi, Andrea F.; Jannoun, Ghina; Congedo, Pietro M.: Kriging-sparse polynomial dimensional decomposition surrogate model with adaptive refinement (2019)

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