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Wan, J.;Zabaras, N.
Stochastic Input Model generation using Bayesian Network Learning
Journal of Computational Physics
2014
272
664-685
Chen, Peng;Zabaras, Nicholas
Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification
Communications in Computational Physics
2013
14
04
851-878
Bilionis, Ilias;Zabaras, Nicholas;Konomi, Bledar A.;Lin, Guang
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification
Journal of Computational Physics
2013
241
212-239
Chen, Peng;Zabaras, Nicholas
A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media
Journal of Computational Physics
2013
250
616-643
Bilionis, Ilias;Zabaras, Nicholas
A stochastic optimization approach to coarse-graining using a relative-entropy framework
The Journal of Chemical Physics
2013
138
4
044313
Wan, Jiang;Zabaras, Nicholas
A probabilistic graphical model approach to stochastic multiscale partial differential equations
Journal of Computational Physics
2013
250
477-510
Kristensen, Jesper;Bilionis, Ilias;Zabaras, Nicholas
Relative entropy as model selection tool in cluster expansions
Phys. Rev. B
2013
87
17
Chen, Peng;Zabaras, Nicholas
Uncertainty quantification for multiscale disk forging of polycrystal materials using probabilistic graphical model techniques
Computational Materials Science
2014
84
278-292
Bilionis, I;Zabaras, N
Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective
Inverse Problems
2013
30
1
015004
Kristensen, Jesper;Zabaras, Nicholas J.
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method
Computer Physics Communications
2014
185
11
2885-2892
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