Gaussian functional regression for output prediction: model assimilation and experimental design

DOI10.1016/j.jcp.2015.12.035zbMath1351.62094OpenAlexW2492842680MaRDI QIDQ729481

D. Kharzeev

Publication date: 20 December 2016

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2015.12.035

zbMATH Keywords

model reductionreduced basis methodexperimental designGaussian functional regressionmulti-fidelity models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Optimal statistical designs (62K05) Bayesian inference (62F15)

Related Items (6)

Reduced basis methods for time-dependent problems ⋮ Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis ⋮ A nonintrusive reduced order modelling approach using proper orthogonal decomposition and locally adaptive sparse grids ⋮ Reduced order modeling for nonlinear structural analysis using Gaussian process regression ⋮ Data-driven reduced order modeling for time-dependent problems ⋮ A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems

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