Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration

DOI10.4208/cicp.OA-2020-0060zbMath1473.62089OpenAlexW3102762050MaRDI QIDQ5163220

John D. Jakeman, Peter Zaspel, Helmut Harbrecht

Publication date: 3 November 2021

Published in: Communications in Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4208/cicp.oa-2020-0060

zbMATH Keywords

Gaussian processradial basis functionBayesian inferenceactive learninguncertainty quantificationexperimental design

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical interpolation (65D05) Response surface designs (62K20)

Related Items (1)

Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems

Uses Software

• Matlab

Cites Work

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