Advances in the Sequential Design of Computer Experiments Based on Active Learning

DOI10.1080/03610920903518848zbMath1318.62264OpenAlexW1985881593MaRDI QIDQ2892647

J. Andres Christen, Bruno Sansó

Publication date: 19 June 2012

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920903518848

zbMATH Keywords

Gaussian processescomputer experimentsactive learningsurrogate modelssequential design

Mathematics Subject Classification ID

Related Items (7)

A mapping-based universal kriging model for order-of-addition experiments in drug combination studies ⋮ Applying kriging proxies for Markov chain Monte Carlo in reservoir simulation ⋮ Adaptive Simulation Selection for the Discovery of the Ground State Line of Binary Alloys with a Limited Computational Budget ⋮ Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model ⋮ Non-parametric Sampling Approximation via Voronoi Tessellations ⋮ Iterative construction of Gaussian process surrogate models for Bayesian inference ⋮ Sequential tuning of complex computer models

Uses Software

• t-walk
• Rtwalk

Cites Work

• The design and analysis of computer experiments.
• Comparison of designs for computer experiments
• A general purpose sampling algorithm for continuous distributions (the t-walk)
• Bayesian Calibration of Computer Models
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• Inferring climate system properties using a computer model

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