Nicholas Zabaras
Orcid: 0000-0003-3144-8388
According to our database1,
Nicholas Zabaras
authored at least 53 papers
between 1999 and 2022.
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Bibliography
2022
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems.
J. Comput. Phys., 2022
2021
J. Comput. Phys., 2021
CoRR, 2021
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning.
CoRR, 2021
2020
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks.
J. Comput. Phys., 2020
Embedded-physics machine learning for coarse-graining and collective variable discovery without data.
CoRR, 2020
2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data.
J. Comput. Phys., 2019
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks.
J. Comput. Phys., 2019
Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion.
J. Comput. Phys., 2019
Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields.
CoRR, 2019
2018
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification.
J. Comput. Phys., 2018
Parallel probabilistic graphical model approach for nonparametric Bayesian inference.
J. Comput. Phys., 2018
Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems.
Comput. Phys. Commun., 2018
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification.
CoRR, 2018
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media.
CoRR, 2018
2017
2016
J. Comput. Phys., 2016
J. Comput. Phys., 2016
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination.
J. Comput. Phys., 2016
Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions.
J. Comput. Phys., 2016
Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory.
J. Comput. Phys., 2016
2015
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference.
J. Comput. Phys., 2015
2014
J. Comput. Phys., 2014
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method.
Comput. Phys. Commun., 2014
2013
A probabilistic graphical model approach to stochastic multiscale partial differential equations.
J. Comput. Phys., 2013
A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media.
J. Comput. Phys., 2013
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification.
J. Comput. Phys., 2013
2012
SIAM J. Sci. Comput., 2012
Multi-output local Gaussian process regression: Applications to uncertainty quantification.
J. Comput. Phys., 2012
2011
J. Comput. Phys., 2011
A stochastic mixed finite element heterogeneous multiscale method for flow in porous media.
J. Comput. Phys., 2011
2010
An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations.
J. Comput. Phys., 2010
2009
Stat. Anal. Data Min., 2009
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations.
J. Comput. Phys., 2009
A stochastic multiscale framework for modeling flow through random heterogeneous porous media.
J. Comput. Phys., 2009
2008
A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach.
J. Comput. Phys., 2008
A stabilized stochastic finite element second-order projection method for modeling natural convection in random porous media.
J. Comput. Phys., 2008
A non-linear dimension reduction methodology for generating data-driven stochastic input models.
J. Comput. Phys., 2008
2007
J. Comput. Phys., 2007
Modeling the growth and interaction of multiple dendrites in solidification using a level set method.
J. Comput. Phys., 2007
J. Comput. Phys., 2007
J. Comput. Phys., 2007
Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method.
J. Comput. Phys., 2007
Comput. Sci. Eng., 2007
2006
Modelling dendritic solidification with melt convection using the extended finite element method.
J. Comput. Phys., 2006
A stochastic variational multiscale method for diffusion in heterogeneous random media.
J. Comput. Phys., 2006
1999