Nicholas Zabaras

According to our database1, Nicholas Zabaras authored at least 49 papers between 1999 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 




Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks.
J. Comput. Phys., 2020

Transformers for Modeling Physical Systems.
CoRR, 2020

Solving inverse problems using conditional invertible neural networks.
CoRR, 2020

Multi-fidelity Generative Deep Learning Turbulent Flows.
CoRR, 2020

Embedded-physics machine learning for coarse-graining and collective variable discovery without data.
CoRR, 2020

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

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

Predictive Collective Variable Discovery with Deep Bayesian Models.
CoRR, 2018

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media.
CoRR, 2018

Structured Bayesian Gaussian process latent variable model.
CoRR, 2018

Predictive coarse-graining.
J. Comput. Phys., 2017

Special Issue: Predictive multiscale materials modeling.
J. Comput. Phys., 2017

Manifold learning for the emulation of spatial fields from computational models.
J. Comput. Phys., 2016

Special Issue: Big data and predictive computational modeling.
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

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference.
J. Comput. Phys., 2015

A probabilistic graphical model based stochastic input model construction.
J. Comput. Phys., 2014

Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method.
Comput. Phys. Commun., 2014

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

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

Kernel principal component analysis for stochastic input model generation.
J. Comput. Phys., 2011

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

An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations.
J. Comput. Phys., 2010

A statistical learning approach for the design of polycrystalline materials.
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

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

A level set simulation of dendritic solidification of multi-component alloys.
J. Comput. Phys., 2007

Modeling the growth and interaction of multiple dendrites in solidification using a level set method.
J. Comput. Phys., 2007

Multiscale modeling of alloy solidification using a database approach.
J. Comput. Phys., 2007

Sparse grid collocation schemes for stochastic natural convection problems.
J. Comput. Phys., 2007

Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method.
J. Comput. Phys., 2007

An Information-Theoretic Approach to Stochastic Materials Modeling.
Comput. Sci. Eng., 2007

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

Using Objects to Model Finite Deformation Plasticity.
Eng. Comput., 1999