ooDACE toolbox: a flexible object-oriented Kriging implementation. When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensive research and many extensions have been proposed, e.g., co-Kriging, stochastic Kriging, blind Kriging, etc. However, few Kriging implementations are publicly available and tailored towards scientists and engineers. Furthermore, no Kriging toolbox exists that unifies several Kriging flavors. This paper addresses this need by presenting an efficient object-oriented Kriging implementation and several Kriging extensions, providing a flexible and easily extendable framework to test and implement new Kriging flavors while reusing as much code as possible.

References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  2. Wauters, Jolan; Keane, Andy; Degroote, Joris: Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization (2020)
  3. Mons, Vincent; Wang, Qi; Zaki, Tamer A.: Kriging-enhanced ensemble variational data assimilation for scalar-source identification in turbulent environments (2019)
  4. Van Steenkiste, Tom; van der Herten, Joachim; Couckuyt, Ivo; Dhaene, Tom: Sequential sensitivity analysis of expensive black-box simulators with metamodelling (2018)
  5. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  6. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
  7. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Shape optimization of a cyclone separator using multi-objective surrogate-based optimization (2016)
  8. Ulaganathan, Selvakumar; Couckuyt, I.; Dhaene, T.; Degroote, J.; Laermans, E.: High dimensional kriging metamodelling utilising gradient information (2016)
  9. Couckuyt, Ivo; Dhaene, Tom; Demeester, Piet: ooDACE toolbox: a flexible object-oriented Kriging implementation (2014)