Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation
From MaRDI portal
Publication:1683081
DOI10.1016/j.ejor.2017.03.042zbMath1378.62039OpenAlexW2599820605MaRDI QIDQ1683081
Publication date: 6 December 2017
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10150/626021
Sequential statistical design (62L05) Prediction theory (aspects of stochastic processes) (60G25) Response surface designs (62K20)
Related Items (max. 100)
Generalized polynomial chaos-informed efficient stochastic kriging ⋮ Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise ⋮ Probabilistic bisection with spatial metamodels
Uses Software
Cites Work
- Unnamed Item
- A method for the updating of stochastic Kriging metamodels
- Switching regression metamodels in stochastic simulation
- Sequential design of computer experiments for the estimation of a probability of failure
- Global sensitivity analysis of stochastic computer models with joint metamodels
- Fast update of conditional simulation ensembles
- A semi-parametric approach to dual modeling when no replication exists
- Efficient global optimization of expensive black-box functions
- The design and analysis of computer experiments.
- A new loss function for multi-response optimization with model parameter uncertainty and implementation errors
- Regression and Kriging metamodels with their experimental designs in simulation: a review
- Batch sequential designs for computer experiments
- Asymptotic analysis of the learning curve for Gaussian process regression
- Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping
- Approximation of IMSE-optimal Designs via Quadrature Rules and Spectral Decomposition
- Spectral Approximation of the IMSE Criterion for Optimal Designs in Kernel-Based Interpolation Models
- Efficient VaR and CVaR Measurement via Stochastic Kriging
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Stochastic Kriging for Simulation Metamodeling
- Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction
- Nested Latin hypercube designs
- Orthogonal Array-Based Latin Hypercubes
- Uniform Design: Theory and Application
- The effects of common random numbers on stochastic kriging metamodels
- Bayesian Kriging Analysis and Design for Stochastic Simulations
- Application-driven sequential designs for simulation experiments: Kriging metamodelling
- Planning Queueing Simulations
- A Simulation-Based Optimization Framework for Urban Transportation Problems
- Stochastic kriging with biased sample estimates
- Enhancing Stochastic Kriging Metamodels with Gradient Estimators
- An Adaptive Exploration-Exploitation Algorithm for Constructing Metamodels in Random Simulation Using a Novel Sequential Experimental Design
- Design and analysis of simulation experiments
This page was built for publication: Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation