Replication or Exploration? Sequential Design for Stochastic Simulation Experiments
From MaRDI portal
Publication:6621613
DOI10.1080/00401706.2018.1469433MaRDI QIDQ6621613
Michael Ludkovski, Jiangeng Huang, Robert B. Gramacy, Mickaël Binois
Publication date: 18 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A method for the updating of stochastic Kriging metamodels
- Computational enhancements to Bayesian design of experiments using Gaussian processes
- Design of computer experiments: space filling and beyond
- Exact optimal designs for computer experiments via kriging metamodelling
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Optimal design for correlated processes with input-dependent noise
- Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation
- Computer experiment designs for accurate prediction
- The design and analysis of computer experiments
- Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise
- Microcolony and biofilm formation as a survival strategy for bacteria
- Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
- Introduction to computational social science. Principles and applications
- Spectral Approximation of the IMSE Criterion for Optimal Designs in Kernel-Based Interpolation Models
- Stochastic Kriging for Simulation Metamodeling
- Discrete Optimization via Simulation Using COMPASS
- Stochastic intrinsic Kriging for simulation metamodeling
- Strictly Proper Scoring Rules, Prediction, and Estimation
- A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations
- Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation
- Design and analysis of simulation experiments
Related Items (8)
Active Learning for Deep Gaussian Process Surrogates ⋮ On-site surrogates for large-scale calibration ⋮ Heteroscedastic Gaussian process regression for material structure-property relationship modeling ⋮ Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models ⋮ Distance-Distributed Design for Gaussian Process Surrogates ⋮ Gaussian Process Assisted Active Learning of Physical Laws ⋮ Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments ⋮ Covariance parameter estimation of Gaussian processes with approximated functional inputs
This page was built for publication: Replication or Exploration? Sequential Design for Stochastic Simulation Experiments