APIK: Active Physics-Informed Kriging Model with Partial Differential Equations

DOI10.1137/20M1389285zbMath1490.60078arXiv2012.11798OpenAlexW3118053773WikidataQ114074114 ScholiaQ114074114MaRDI QIDQ5075236

Chuck Zhang, Jialei Chen, Zhehui Chen, C. F. Jeff Wu

Publication date: 10 May 2022

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

Full work available at URL: https://arxiv.org/abs/2012.11798

zbMATH Keywords

expectation-maximization algorithmGaussian process regressionderivative processpseudopoints

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Applications of statistics to social sciences (62P25) Bayesian inference (62F15) Response surface designs (62K20)

Related Items (1)

Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis

Uses Software

• EnKF
• L-BFGS
• COMSOL
• DiceKriging

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