SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities: Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs. Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression. Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others). Surrogate-based optimization: efficient global optimization (EGO) algorithm. Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.

References in zbMATH (referenced in 16 articles )

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  1. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  2. García-García, José Carlos; García-Ródenas, Ricardo; Codina, Esteve: A surrogate-based cooperative optimization framework for computationally expensive black-box problems (2020)
  3. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  4. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Balancing global and local search in parallel efficient global optimization algorithms (2017)
  5. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Pseudo expected improvement criterion for parallel EGO algorithm (2017)
  6. Gogu, Christian; Passieux, Jean-Charles: Efficient surrogate construction by combining response surface methodology and reduced order modeling (2013) ioport
  7. Regis, Rommel G.; Shoemaker, Christine A.: A quasi-multistart framework for global optimization of expensive functions using response surface models (2013)
  8. Song, Hyeongjin; Choi, K. K.; Lee, Ikjin; Zhao, Liang; Lamb, David: Adaptive virtual support vector machine for reliability analysis of high-dimensional problems (2013)
  9. Viana, Felipe A. C.; Haftka, Raphael T.; Watson, Layne T.: Efficient global optimization algorithm assisted by multiple surrogate techniques (2013)
  10. Degroote, Joris; Couckuyt, Ivo; Vierendeels, Jan; Segers, Patrick; Dhaene, Tom: Inverse modelling of an aneurysm’s stiffness using surrogate-based optimization and fluid-structure interaction simulations (2012)
  11. Li, Jian; Wang, Hai; Kim, Nam H.: Doubly weighted moving least squares and its application to structural reliability analysis (2012) ioport
  12. Viana, Felipe A. C.; Haftka, Raphael T.; Watson, Layne T.: Sequential sampling for contour estimation with concurrent function evaluations (2012)
  13. Cho, Young-Chang; Jayaraman, Balaji; Viana, Felipe A. C.; Haftka, Raphael T.; Shyy, Wei: Surrogate modelling for characterising the performance of a dielectric barrier discharge plasma actuator (2010)
  14. Gogu, Christian; Haftka, Raphael; Le Riche, Rodolphe; Molimard, Jerome: Effect of approximation fidelity on vibration-based elastic constants identification (2010) ioport
  15. Pattabhiraman, Sriram; Levesque, George; Kim, Nam H.; Arakere, Nagaraj K.: Uncertainty analysis for rolling contact fatigue failure probability of silicon nitride ball bearings (2010)
  16. Viana, Felipe A. C.; Haftka, Raphael T.; Valder, Steffen jun.: Multiple surrogates: how cross-validation errors can help us to obtain the best predictor (2010) ioport

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