Pages that link to "Item:Q1281563"

The following pages link to Efficient global optimization of expensive black-box functions (Q1281563):

Displaying 50 items.

• A novel nested stochastic Kriging model for response noise quantification and reliability analysis (Q2237444) (← links)
• Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian processes (Q2237721) (← links)
• Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation (Q2240278) (← links)
• A combined radial basis function and adaptive sequential sampling method for structural reliability analysis (Q2241757) (← links)
• Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems (Q2241996) (← links)
• DEFT-FUNNEL: an open-source global optimization solver for constrained grey-box and black-box problems (Q2245001) (← links)
• Rapid design of metamaterials via multitarget Bayesian optimization (Q2245155) (← links)
• Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger (Q2245306) (← links)
• Bayesian optimization using deep Gaussian processes with applications to aerospace system design (Q2245694) (← links)
• Bayesian optimization of variable-size design space problems (Q2245699) (← links)
• Efficient global optimization method via clustering/classification methods and exploration strategy (Q2245706) (← links)
• An improved Bayesian collocation method for steady-state response analysis of structural dynamic systems with large interval uncertainties (Q2245998) (← links)
• A virtual model architecture for engineering structures with twin extended support vector regression (T-X-SVR) method (Q2246326) (← links)
• Engineering design applications of surrogate-assisted optimization techniques (Q2254199) (← links)
• Contour estimation via two fidelity computer simulators under limited resources (Q2259100) (← links)
• One-dimensional nested maximin designs (Q2269596) (← links)
• Batch sequential designs for computer experiments (Q2270267) (← links)
• Efficient computation of expected hypervolume improvement using box decomposition algorithms (Q2274866) (← links)
• Kernel interpolation (Q2275648) (← links)
• Probability and convex set hybrid reliability analysis based on active learning kriging model (Q2278777) (← links)
• Coupling multi-fidelity Kriging and model-order reduction for the construction of virtual charts (Q2281513) (← links)
• A survey on kriging-based infill algorithms for multiobjective simulation optimization (Q2289950) (← links)
• Gaussian field on the symmetric group: prediction and learning (Q2293716) (← links)
• Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (Q2294603) (← links)
• Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing's type (Q2296783) (← links)
• A Kriging-based active learning algorithm for mechanical reliability analysis with time-consuming and nonlinear response (Q2298781) (← links)
• High-dimensional Bayesian optimization with projections using quantile Gaussian processes (Q2300637) (← links)
• Iterative construction of Gaussian process surrogate models for Bayesian inference (Q2301102) (← links)
• On the choice of the low-dimensional domain for global optimization via random embeddings (Q2301180) (← links)
• A multiobjective stochastic simulation optimization algorithm (Q2301958) (← links)
• Combining Bayesian optimization and Lipschitz optimization (Q2303661) (← links)
• An adaptive divergence-based method for structural reliability analysis via multiple kriging models (Q2306804) (← links)
• Sequential approximate optimization for design under uncertainty problems utilizing Kriging metamodeling in augmented input space (Q2308910) (← links)
• Calibration experimental design considering field response and model uncertainty (Q2309083) (← links)
• Optimization of expensive black-box problems via gradient-enhanced Kriging (Q2310272) (← links)
• A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (Q2310508) (← links)
• Adaptive Gaussian process emulators for efficient reliability analysis (Q2310644) (← links)
• Extreme value oriented random field discretization based on an hybrid polynomial chaos expansion -- Kriging approach (Q2310861) (← links)
• Sparse kernel deep stacking networks (Q2319474) (← links)
• Estimating percentiles in computer experiments: a comparison of sequential-adaptive designs and fixed designs (Q2320886) (← links)
• Maximum likelihood estimation for Gaussian processes under inequality constraints (Q2323946) (← links)
• A supermartingale approach to Gaussian process based sequential design of experiments (Q2325344) (← links)
• Global optimization using Gaussian processes to estimate biological parameters from image data (Q2328256) (← links)
• Molecular discovery by optimal sequential search (Q2334495) (← links)
• A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization (Q2342949) (← links)
• Sequential approximate optimization for discrete design variable problems using radial basis function network (Q2344703) (← links)
• Hybrid reliability analysis with both random and probability-box variables (Q2353316) (← links)
• Multivariate versus univariate Kriging metamodels for multi-response simulation models (Q2356270) (← links)
• An adaptive-topology ensemble algorithm for engineering optimization problems (Q2357899) (← links)
• Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems (Q2357949) (← links)