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 single-loop procedure for time-variant reliability analysis based on kriging model (Q1985150) (← links)
• Sparse polynomial chaos expansions via compressed sensing and D-optimal design (Q1985532) (← links)
• Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints (Q1987853) (← links)
• pBO-2GP-3B: a batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics (Q1987855) (← links)
• Kriging-assisted topology optimization of crash structures (Q1987933) (← links)
• Systems of Gaussian process models for directed chains of solvers (Q1988026) (← links)
• One-dimensional modeling of fractional flow reserve in coronary artery disease: uncertainty quantification and Bayesian optimization (Q1988087) (← links)
• Unified uncertainty analysis under probabilistic, evidence, fuzzy and interval uncertainties (Q1988196) (← links)
• Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm (Q1990872) (← links)
• Emulating dynamic non-linear simulators using Gaussian processes (Q2002727) (← links)
• Modelling of a thermomechanically coupled forming process based on functional outputs from a finite element analysis and from experimental measurements (Q2006885) (← links)
• Bayesian model calibration and optimization of surfactant-polymer flooding (Q2009830) (← links)
• Machine-learning in optimization of expensive black-box functions (Q2012143) (← links)
• Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (Q2013835) (← links)
• Proper orthogonal decomposition, surrogate modelling and evolutionary optimization in aerodynamic design (Q2015955) (← links)
• Gradient-based methods for uncertainty quantification in hypersonic flows (Q2015969) (← links)
• Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (Q2020786) (← links)
• An efficient Kriging based method for time-dependent reliability based robust design optimization via evolutionary algorithm (Q2020995) (← links)
• A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources (Q2021998) (← links)
• Gaussian process optimization with failures: classification and convergence proof (Q2022175) (← links)
• Expected improvement for expensive optimization: a review (Q2022176) (← links)
• Stochastic optimization with adaptive restart: a framework for integrated local and global learning (Q2022223) (← links)
• Sequential model based optimization of partially defined functions under unknown constraints (Q2022235) (← links)
• Global optimization via inverse distance weighting and radial basis functions (Q2023666) (← links)
• An extended two-stage sequential optimization approach: properties and performance (Q2023982) (← links)
• Dataset2Vec: learning dataset meta-features (Q2036744) (← links)
• Black-box combinatorial optimization using models with integer-valued minima (Q2043440) (← links)
• Surrogate optimization of deep neural networks for groundwater predictions (Q2046338) (← links)
• Global optimization based on active preference learning with radial basis functions (Q2051251) (← links)
• MODES: model-based optimization on distributed embedded systems (Q2051343) (← links)
• Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation (Q2058770) (← links)
• Advanced computational technique based on kriging and Polynomial Chaos Expansion for structural stability of mechanical systems with uncertainties (Q2061435) (← links)
• Recursive modified pattern search on high-dimensional simplex: a blackbox optimization technique (Q2061776) (← links)
• Deterministic global optimization with Gaussian processes embedded (Q2062323) (← links)
• Reliable crack detection in a rotor system with uncertainties via advanced simulation models based on kriging and polynomial chaos expansion (Q2063401) (← links)
• SMGO: a set membership approach to data-driven global optimization (Q2065241) (← links)
• Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models (Q2066738) (← links)
• Bayesian optimization of functional output in inverse problems (Q2069152) (← links)
• GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration (Q2069161) (← links)
• Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (Q2070360) (← links)
• Fast selection of nonlinear mixed effect models using penalized likelihood (Q2072413) (← links)
• Analyzing stochastic computer models: a review with opportunities (Q2075795) (← links)
• Biobjective robust simulation-based optimization for unconstrained problems (Q2077985) (← links)
• Uncertainty quantification of a computer model for binary black hole formation (Q2078270) (← links)
• Cylinder drag minimization through wall actuation: a Bayesian optimization approach (Q2083875) (← links)
• A confidence-based aerospace design approach robust to structural turbulence closure uncertainty (Q2084092) (← links)
• Bayesian optimization approaches for identifying the best genotype from a candidate population (Q2084441) (← links)
• Sequential design strategy for kriging and cokriging-based machine learning in the context of reservoir history-matching (Q2085079) (← links)
• Batch sequential adaptive designs for global optimization (Q2089024) (← links)
• Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces (Q2096838) (← links)