DiceOptim: Kriging-based optimization for computer experiments Expected Improvement. EGO algorithm. Multipoints EI and parallelized versions of EGO: Constant Liars. Criteria and algorithms for Noisy Kriging-based Optimization , including AEI, AKG, EQI, and EI with plugin.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
- Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
- De Lozzo, Matthias; Marrel, Amandine: Estimation of the derivative-based global sensitivity measures using a Gaussian process metamodel (2016)
- Chevalier, Clément; Emery, Xavier; Ginsbourger, David: Fast update of conditional simulation ensembles (2015)
- Kamiński, Bogumił: A method for the updating of stochastic Kriging metamodels (2015)
- Picheny, Victor: Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction (2015)
- Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2014)
- Kleijnen, Jack P.C.; Mehdad, Ehsan: Multivariate versus univariate Kriging metamodels for multi-response simulation models (2014)
- Viana, Felipe A.C.; Haftka, Raphael T.; Watson, Layne T.: Efficient global optimization algorithm assisted by multiple surrogate techniques (2013)
- 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)
- Muehlenstaedt, Thomas; Roustant, Olivier; Carraro, Laurent: Data-driven kriging models based on FANOVA-decomposition (2012)