Aspects of the Matlab toolbox DACE

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A new learning function for Kriging and its applications to solve reliability problems in engineering (2015, methodology): “The process of calculating µĜ(x) and σĜ(x) above can be implemented by MATLAB toolbox DACE [27,28].”
Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis (2009, methodology): “Results are obtained using the MATLAB Kriging toolbox DACE [47].”
Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives (2012, methodology): “Otherwise compute the likelihood of the RBF interpolation using standard methods [30,31].”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, methodology): “a Kriging model still takes 10 and 100 s when the number of training data is 250 and 550, respectively, as shown in [54].”
Automated configuration of algorithms for solving hard computational problems (2009, methodology): “The exact equations used in both SKO and the DACE toolbox implement methods to deal with ill conditioning; we refer the reader to the original publications for details (Huang et al., 2006; Bartz-Beielstein, 2006; Lophaven et al., 2002).”
DACE - A Matlab Kriging Toolbox (2002, background): “See [4] for a thorough discussion of properties of different correlation models and the optimization problem (2.”