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 101 articles )

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  1. Balesdent, Mathieu; Morio, Jér^ome; Brevault, Loïc: Rare event probability estimation in the presence of epistemic uncertainty on input probability distribution parameters (2016)
  2. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  3. 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)
  4. Yang, Qinwen; Xue, Deyi: A weighted sequential sampling method considering influences of sample qualities in input and output parameter spaces for global optimization (2015)
  5. Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)
  6. Couckuyt, Ivo; Dhaene, Tom; Demeester, Piet: ooDACE toolbox: a flexible object-oriented Kriging implementation (2014)
  7. Kleijnen, Jack P.C.; Mehdad, Ehsan: Multivariate versus univariate Kriging metamodels for multi-response simulation models (2014)
  8. Long, C.C.; Marsden, A.L.; Bazilevs, Y.: Shape optimization of pulsatile ventricular assist devices using FSI to minimize thrombotic risk (2014)
  9. 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)
  10. Biron, Guillaume; Vadean, Aurelian; Tudose, Lucian: Optimal design of interference fit assemblies subjected to fatigue loads: a sequential approximate multi-objective optimization approach (2013)
  11. Castrillón-Candás, Julio E.; Li, Jun; Eijkhout, Victor: A discrete adapted hierarchical basis solver for radial basis function interpolation (2013)
  12. Chassaing, Jean-Camille; Nogueira, Xesús; Khelladi, Sofiane: Moving Kriging reconstruction for high-order finite volume computation of compressible flows (2013)
  13. Horng, Shih-Cheng; Lin, Shin-Yeu: Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation (2013)
  14. Jia, Gaofeng; Taflanidis, Alexandros A.: Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment (2013)
  15. Kunakote, Tawatchai; Bureerat, Sujin: Surrogate-assisted multiobjective evolutionary algorithms for structural shape and sizing optimisation (2013)
  16. Regis, Rommel G.; Shoemaker, Christine A.: A quasi-multistart framework for global optimization of expensive functions using response surface models (2013)
  17. Rosenbaum, Benjamin; Schulz, Volker: Efficient response surface methods based on generic surrogate models (2013)
  18. Viana, Felipe A.C.; Haftka, Raphael T.; Watson, Layne T.: Efficient global optimization algorithm assisted by multiple surrogate techniques (2013)
  19. Zimmermann, R.: On the maximum likelihood training of gradient-enhanced spatial Gaussian processes (2013)
  20. Abramson, Mark A.; Asaki, Thomas J.; Dennis, John E.jun.; Magallanez, Raymond jun.; Sottile, Matthew J.: An efficient class of direct search surrogate methods for solving expensive optimization problems with CPU-time-related functions (2012)

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