# Nicholas Zabaras

According to our database

Collaborative distances:

^{1}, Nicholas Zabaras authored at least 49 papers between 1999 and 2020.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2020

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks.

J. Comput. Phys., 2020

CoRR, 2020

CoRR, 2020

CoRR, 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

CoRR, 2018

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media.

CoRR, 2018

CoRR, 2018

2017

J. Comput. Phys., 2017

J. Comput. Phys., 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

Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification.

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

Eng. Comput., 1999