Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference-Reference-Cited by-同舟云学术

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference

Published:2015-03 Issue: Volume:284 Page:291-333
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Chen Peng,Zabaras Nicholas,Bilionis Ilias

Funder

Royal Society

Wolfson Research Merit Award

EPSRC

Technische Universität München

Institute for Advanced Study

German Excellence Initiative

European Union Seventh Framework Programme

OSD

AFOSR MURI09

US Department of Energy, Office of Science, Advanced Scientific Computing Research

National Science Foundation

Office of Science of the U.S. Department of Energy

NSF

NCSA

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference48 articles.

1. A multiscale finite element method for elliptic problems in composite materials and porous media;Hou;J. Comput. Phys.,1997

2. Numerical methods for multiscale elliptic problems;Ming;J. Comput. Phys.,2006

3. The heterogeneous multiscale finite element method for elliptic homogenization problems in perforated domains;Henning;Numer. Math.,2009

4. A stochastic mixed finite element heterogeneous multiscale method for flow in porous media;Ma;J. Comput. Phys.,2011

5. Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification;Bilionis;J. Comput. Phys.,2013

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