HomePosts Tagged "uncertainty quantification"

Tag Archives: uncertainty quantification

New article: A Nonparametric Belief Propagation Method for Uncertainty Quantification with Applications for Flow in Random Porous Media

Article: Chen P, Zabaras N, (2013) A Nonparametric Belief Propagation Method for Uncertainty Quantification with Applications for Flow in Random Porous Media. Journal of Computational Physics, 250; 616-643 DOI Abstract: A probabilistic graphical model ...
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October 1, 2013UncategorizedLoopy belief propagation, Nonparametric belief propagation, Porous media flow, Predictive modeling, Probabilistic graphical models, uncertainty quantification

New article: A Probabilistic Graphical Model Approach to Stochastic Multiscale Partial Differential Equations

Article: Wan J, Zabaras N, (2013) A Probabilistic Graphical Model Approach to Stochastic Multiscale Partial Differential Equations. Journal of Computational Physics, 250; 477-510 DOI Abstract: We develop a probabilistic graphical model based methodology to ...
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October 1, 2013Uncategorizedbayesian, Belief propagation, Multiscale modeling, Probabilistic graphical models, Sequential Monte Carlo, stochastic partial differential equations, uncertainty quantification

New article: Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification

Article: Chen P, Zabaras N, (2013) Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification, Communications in Computational Physics, 14 (4); 851-878 DOI Abstract: We develop an efficient, adaptive locally weighted projection regression (ALWPR) ...
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October 1, 2013Uncategorizedadaptivity, Locally weighted projection regression, multi-output, uncertainty quantification

New article: Multi-output Separable Gaussian Process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

Article: Bilionis I, Zabaras N, Konomi BA, and Lin G, (2013) Multi-output Separable Gaussian Process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. J. of Computational Physics, 241; 212-239 DOI Abstract: Computer codes ...
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May 15, 2013Uncategorizedbayesian, gaussian process, Kronecker product, Separable covariance function, stochastic partial differential equations, Surrogate models, uncertainty quantification

New article: Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification

Article: Bilionis I and Zabaras N (2012). “Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification.” Siam Journal on Scientific Computing 34(6): B881-B908. DOI Abstract: We develop a Bayesian uncertainty quantification framework using a local ...
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December 13, 2012Uncategorizedadaptivity, efficient, elliptic problems, flow, generalized polynomial chaos, heterogeneous porous-media, models, multioutput, probabilistic collocation method, random input data, relevance vector machine, sparse bayesian learning, sparse grids, stochastic differential-equations, uncertainty quantification

New article: Multi-output local Gaussian process regression: Applications to uncertainty quantification

Article: Bilionis I and Zabaras N (2012). “Multi-output local Gaussian process regression: Applications to uncertainty quantification.” Journal of Computational Physics 231(17): 5718-5746. DOI Abstract: We develop an efficient, Bayesian Uncertainty Quantification framework using a ...
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July 15, 2012Uncategorizedadaptivity, bayesian, collocation method, design, gaussian process, generalized polynomial chaos, models, multi-element, multi-output, random input data, stochastic differential-equations, stochastic partial differential equations, uncertainty quantification

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Posts Tagged: uncertainty quantification