Adaptive Numerical Designs for the Calibration of Computer Codes

DOI10.1137/15M1033162zbMath1386.65058arXiv1502.07252OpenAlexW3101048699MaRDI QIDQ4636370

Guillaume Damblin, Pierre Barbillon, Merlin Keller, Alberto Pasanisi, Eric Parent

Publication date: 19 April 2018

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1502.07252

zbMATH Keywords

Kullback-Leibler divergenceBayesian calibrationGaussian process emulatorexpected improvement criterion

Mathematics Subject Classification ID

Gaussian processes (60G15) Sequential statistical design (62L05) Response surface designs (62K20)

Related Items

Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework, Iterative method for tuning complex simulation code

Uses Software

• EGO

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
• Design of computer experiments: space filling and beyond
• Calibrating a large computer experiment simulating radiative shock hydrodynamics
• Convergence properties of the expected improvement algorithm with fixed mean and covariance functions
• Efficient global optimization of expensive black-box functions
• Interpolation of spatial data. Some theory for kriging
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Error estimates and condition numbers for radial basis function interpolation
• Simultaneous Kriging-based estimation and optimization of mean response
• Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes
• Bayesian Calibration of Computer Models
• Learning about physical parameters: the importance of model discrepancy
• Introduction to Derivative-Free Optimization
• Bayesian Forecasting for Complex Systems Using Computer Simulators
• Combining Field Data and Computer Simulations for Calibration and Prediction
• A Frequentist Approach to Computer Model Calibration
• A statistical method for tuning a computer code to a data base.
• Modularization in Bayesian analysis, with emphasis on analysis of computer models