Using Objects to Model Finite Deformation Plasticity. - Nicholas Zabaras; Akkaram Srikanth (Eng. Comput., 1999)

A stochastic variational multiscale method for diffusion in heterogeneous random media. - Badrinarayanan Velamur Asokan; Nicholas Zabaras (J. Comput. Phys., 2006)

Modelling dendritic solidification with melt convection using the extended finite element method. - Nicholas Zabaras; Baskar Ganapathysubramanian; Lijian Tan (J. Comput. Phys., 2006)

An Information-Theoretic Approach to Stochastic Materials Modeling. - Nicholas Zabaras; Sethuraman Sankaran (Comput. Sci. Eng., 2007)

Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method. - Baskar Ganapathysubramanian; Nicholas Zabaras (J. Comput. Phys., 2007)

Sparse grid collocation schemes for stochastic natural convection problems. - Baskar Ganapathysubramanian; Nicholas Zabaras (J. Comput. Phys., 2007)

Multiscale modeling of alloy solidification using a database approach. - Lijian Tan; Nicholas Zabaras (J. Comput. Phys., 2007)

Modeling the growth and interaction of multiple dendrites in solidification using a level set method. - Lijian Tan; Nicholas Zabaras (J. Comput. Phys., 2007)

A level set simulation of dendritic solidification of multi-component alloys. - Lijian Tan; Nicholas Zabaras (J. Comput. Phys., 2007)

A non-linear dimension reduction methodology for generating data-driven stochastic input models. - Baskar Ganapathysubramanian; Nicholas Zabaras (J. Comput. Phys., 2008)

A stabilized stochastic finite element second-order projection method for modeling natural convection in random porous media. - Xiang Ma; Nicholas Zabaras (J. Comput. Phys., 2008)

A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach. - Nicholas Zabaras; Baskar Ganapathysubramanian (J. Comput. Phys., 2008)

A stochastic multiscale framework for modeling flow through random heterogeneous porous media. - Baskar Ganapathysubramanian; Nicholas Zabaras (J. Comput. Phys., 2009)

An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. - Xiang Ma; Nicholas Zabaras (J. Comput. Phys., 2009)

A statistical learning approach for the design of polycrystalline materials. - Veera Sundararaghavan; Nicholas Zabaras (Stat. Anal. Data Min., 2009)

An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations. - Xiang Ma; Nicholas Zabaras (J. Comput. Phys., 2010)

A stochastic mixed finite element heterogeneous multiscale method for flow in porous media. - Xiang Ma; Nicholas Zabaras (J. Comput. Phys., 2011)

Kernel principal component analysis for stochastic input model generation. - Xiang Ma; Nicholas Zabaras (J. Comput. Phys., 2011)

Multi-output local Gaussian process regression: Applications to uncertainty quantification. - Ilias Bilionis; Nicholas Zabaras (J. Comput. Phys., 2012)

Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification. - Ilias Bilionis; Nicholas Zabaras (SIAM J. Sci. Comput., 2012)

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. - Ilias Bilionis; Nicholas Zabaras; Bledar A. Konomi; Guang Lin 001 (J. Comput. Phys., 2013)

A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media. - Peng Chen; Nicholas Zabaras (J. Comput. Phys., 2013)

A probabilistic graphical model approach to stochastic multiscale partial differential equations. - Jiang Wan; Nicholas Zabaras (J. Comput. Phys., 2013)

Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. - Jesper Kristensen; Nicholas Zabaras (Comput. Phys. Commun., 2014)

A probabilistic graphical model based stochastic input model construction. - Jiang Wan; Nicholas Zabaras (J. Comput. Phys., 2014)

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference. - Peng Chen; Nicholas Zabaras; Ilias Bilionis (J. Comput. Phys., 2015)

Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory. - Manuel Aldegunde; James R. Kermode; Nicholas Zabaras (J. Comput. Phys., 2016)

Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions. - Manuel Aldegunde; Nicholas Zabaras; Jesper Kristensen (J. Comput. Phys., 2016)

A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. - Louis Ellam; Nicholas Zabaras; Mark A. Girolami (J. Comput. Phys., 2016)

Special Issue: Big data and predictive computational modeling. - Phaedon-Stelios Koutsourelakis; Nicholas Zabaras; Michele Girolami (J. Comput. Phys., 2016)

Manifold learning for the emulation of spatial fields from computational models. - Wei W. Xing; Vasileios Triantafyllidis; Akeel A. Shah; Prasanth B. Nair; Nicholas Zabaras (J. Comput. Phys., 2016)

Special Issue: Predictive multiscale materials modeling. - Markos A. Katsoulakis; Nicholas Zabaras (J. Comput. Phys., 2017)

Predictive coarse-graining. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2017)

Structured Bayesian Gaussian process latent variable model. - Steven Atkinson; Nicholas Zabaras (CoRR, 2018)

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. - Shaoxing Mo; Yinhao Zhu; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2018)

Predictive Collective Variable Discovery with Deep Bayesian Models. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (CoRR, 2018)

Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. - Shaoxing Mo; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2018)

Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. - Souvik Chakraborty; Nicholas Zabaras (Comput. Phys. Commun., 2018)

Parallel probabilistic graphical model approach for nonparametric Bayesian inference. - Wonjung Lee; Nicholas Zabaras (J. Comput. Phys., 2018)

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification. - Yinhao Zhu; Nicholas Zabaras (J. Comput. Phys., 2018)

Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields. - Shaoxing Mo; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2019)

Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion. - Steven Atkinson; Nicholas Zabaras (J. Comput. Phys., 2019)

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks. - Nicholas Geneva; Nicholas Zabaras (J. Comput. Phys., 2019)

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. - Yinhao Zhu; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis; Paris Perdikaris (J. Comput. Phys., 2019)

Embedded-physics machine learning for coarse-graining and collective variable discovery without data. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (CoRR, 2020)

Multi-fidelity Generative Deep Learning Turbulent Flows. - Nicholas Geneva; Nicholas Zabaras (CoRR, 2020)

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. - Nicholas Geneva; Nicholas Zabaras (J. Comput. Phys., 2020)

A Bayesian Multiscale Deep Learning Framework for Flows in Random Media. - Govinda Anantha Padmanabha; Nicholas Zabaras (CoRR, 2021)

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning. - Sayan Ghosh; Govinda A. Padmanabha; Cheng Peng; Steven Atkinson; Valeria Andreoli; Piyush Pandita; Thomas Vandeputte; Nicholas Zabaras; Liping Wang (CoRR, 2021)

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. - Zitong Zhou; Nicholas Zabaras; Daniel M. Tartakovsky (CoRR, 2021)

Solving inverse problems using conditional invertible neural networks. - Govinda Anantha Padmanabha; Nicholas Zabaras (J. Comput. Phys., 2021)

Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. - Yingzhi Xia; Nicholas Zabaras (J. Comput. Phys., 2022)

Transformers for modeling physical systems. - Nicholas Geneva; Nicholas Zabaras (Neural Networks, 2022)