DACE

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.


References in zbMATH (referenced in 161 articles )

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  1. 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)
  2. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  3. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  4. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems (2020)
  5. García-García, José Carlos; García-Ródenas, Ricardo; Codina, Esteve: A surrogate-based cooperative optimization framework for computationally expensive black-box problems (2020)
  6. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  7. 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)
  8. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  9. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)
  10. 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)
  11. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  12. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  13. Cortesi, Andrea F.; Jannoun, Ghina; Congedo, Pietro M.: Kriging-sparse polynomial dimensional decomposition surrogate model with adaptive refinement (2019)
  14. 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)
  15. Morse, Llewellyn; Sharif Khodaei, Zahra; Aliabadi, M. H.: A multi-fidelity boundary element method for structural reliability analysis with higher-order sensitivities (2019)
  16. Qian, Hua-Ming; Huang, Hong-Zhong; Li, Yan-Feng: A novel single-loop procedure for time-variant reliability analysis based on Kriging model (2019)
  17. Shi, Yan; Lu, Zhenzhou; Xu, Liyang; Chen, Siyu: An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis (2019)
  18. 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)
  19. Yang, Zan; Qiu, Haobo; Gao, Liang; Jiang, Chen; Zhang, Jinhao: Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems (2019)
  20. Zhang, Jinhao; Xiao, Mi; Gao, Liang: A new method for reliability analysis of structures with mixed random and convex variables (2019)

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