Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs

DOI10.1080/01621459.2014.900250zbMath1368.65017arXiv1402.6350OpenAlexW1995584797MaRDI QIDQ4975627

Matthew Plumlee

Publication date: 7 August 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.6350

zbMATH Keywords

Gaussian processcomputer experimentsimulation experimentlarge-scale experimenthigh-dimensional input

Mathematics Subject Classification ID

Random fields (60G60) Gaussian processes (60G15)

Related Items (9)

On the Instability Issue of Gradient-Enhanced Gaussian Process Emulators for Computer Experiments ⋮ Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code ⋮ Scaled Vecchia Approximation for Fast Computer-Model Emulation ⋮ Sparse polynomial prediction ⋮ A Clustered Gaussian Process Model for Computer Experiments ⋮ Emulating dynamic non-linear simulators using Gaussian processes ⋮ A Bayesian optimization approach to find Nash equilibria ⋮ Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments ⋮ Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments

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