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 187 articles )

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  1. Fuhg, Jan N.; Bouklas, Nikolaos: On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (2022)
  2. Hong, Linxiong; Li, Huacong; Fu, Jiangfeng: A novel surrogate-model based active learning method for structural reliability analysis (2022)
  3. Sinou, J.-J.; Denimal, E.: Reliable crack detection in a rotor system with uncertainties via advanced simulation models based on kriging and polynomial chaos expansion (2022)
  4. Song, Chaolin; Wang, Zeyu; Shafieezadeh, Abdollah; Xiao, Rucheng: BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling (2022)
  5. Yang, Meide; Zhang, Dequan; Wang, Fang; Han, Xu: Efficient local adaptive Kriging approximation method with single-loop strategy for reliability-based design optimization (2022)
  6. Zhang, Jinhao; Xiao, Mi; Li, Peigen; Gao, Liang: Quantile-based topology optimization under uncertainty using kriging metamodel (2022)
  7. Zheng, Liang; Bao, Ji; Xu, Chengcheng; Tan, Zhen: Biobjective robust simulation-based optimization for unconstrained problems (2022)
  8. 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)
  9. Cheng, Kai; Lu, Zhenzhou: Active learning Bayesian support vector regression model for global approximation (2021)
  10. Chen, Hao; Zhang, Yan; Yang, Xue: Uniform projection nested Latin hypercube designs (2021)
  11. Chu, Sheng; Xiao, Mi; Gao, Liang; Zhang, Yan; Zhang, Jinhao: Robust topology optimization for fiber-reinforced composite structures under loading uncertainty (2021)
  12. Denimal, E.; Sinou, J.-J.: Advanced kriging-based surrogate modelling and sensitivity analysis for rotordynamics with uncertainties (2021)
  13. Doan, V. T.; Massa, F.; Tison, T.; Naceur, H.: Coupling of homotopy perturbation method and kriging surrogate model for an efficient fuzzy linear buckling analysis: application to additively manufactured lattice structures (2021)
  14. Fuhg, Jan Niklas; Böhm, Christoph; Bouklas, Nikolaos; Fau, Amelie; Wriggers, Peter; Marino, Michele: Model-data-driven constitutive responses: application to a multiscale computational framework (2021)
  15. 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)
  16. 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)
  17. Hu, Yingshi; Lu, Zhenzhou; Wei, Ning; Jiang, Xia; Zhou, Changcong: Advanced single-loop kriging surrogate model method by combining the adaptive reduction of candidate sample pool for safety lifetime analysis (2021)
  18. 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)
  19. Liang, Yu; Gao, Xiao-Wei; Xu, Bing-Bing; Cui, Miao; Zheng, Bao-Jing: A reduced-order modelling for real-time identification of damages in multi-layered composite materials (2021)
  20. Rathi, Amit Kumar; Chakraborty, Arunasis: Improved moving least square-based multiple dimension decomposition (MDD) technique for structural reliability analysis (2021)

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