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Identification of expensive-to-simulate parametric models using Kriging and stepwise uncertainty reduction.

, , and . CDC, page 5505-5510. IEEE, (2007)

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A new integral loss function for Bayesian optimization., and . CoRR, (2014)A Bayesian approach to constrained single- and multi-objective optimization., , and . J. Glob. Optim., 67 (1-2): 97-133 (2017)Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria., , , and . J. Glob. Optim., 43 (2-3): 373-389 (2009)Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion., , and . LION, volume 6683 of Lecture Notes in Computer Science, page 176-190. Springer, (2011)User Preferences in Bayesian Multi-objective Optimization: The Expected Weighted Hypervolume Improvement Criterion., , and . LOD, volume 11331 of Lecture Notes in Computer Science, page 533-544. Springer, (2018)A Bayesian Approach to Constrained Multi-objective Optimization., , and . LION, volume 8994 of Lecture Notes in Computer Science, page 256-261. Springer, (2015)Designing an Optimal Search Algorithm with Respect to Prior Information., and . Theory and Principled Methods for the Design of Metaheuristics, Springer, (2014)Sequential Design of Multi-Fidelity Computer Experiments: Maximizing the Rate of Stepwise Uncertainty Reduction., , , , , and . Technometrics, 64 (2): 199-209 (2022)Numerical issues in maximum likelihood parameter estimation for Gaussian process regression., , , and . CoRR, (2021)Kriging for indirect measurement with application to flow measurement., , and . IEEE Trans. Instrumentation and Measurement, 55 (1): 343-349 (2006)