Nicholas Zabaras

60 papers Journal 60

Year	Rank	Type	Title / Venue / Authors
2022	J	`jnl`	Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. J. Comput. Phys. Yingzhi Xia, Nicholas Zabaras
2022	J	`jnl`	Transformers for modeling physical systems. Neural Networks Nicholas Geneva, Nicholas Zabaras
2021	J	`jnl`	A Bayesian Multiscale Deep Learning Framework for Flows in Random Media. CoRR Govinda Anantha Padmanabha, Nicholas Zabaras
2021	J	`jnl`	Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. CoRR Yingzhi Xia, Nicholas Zabaras
2021	J	`jnl`	Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. CoRR Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
2021	J	`jnl`	Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning. CoRR Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Valeria Andreoli, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
2021	J	`jnl`	Solving inverse problems using conditional invertible neural networks. J. Comput. Phys. Govinda Anantha Padmanabha, Nicholas Zabaras
2020	J	`jnl`	Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2020	J	`jnl`	Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. J. Comput. Phys. Nicholas Geneva, Nicholas Zabaras
2020	J	`jnl`	Multi-fidelity Generative Deep Learning Turbulent Flows. CoRR Nicholas Geneva, Nicholas Zabaras
2020	J	`jnl`	Solving inverse problems using conditional invertible neural networks. CoRR Govinda Anantha Padmanabha, Nicholas Zabaras
2020	J	`jnl`	Transformers for Modeling Physical Systems. CoRR Nicholas Geneva, Nicholas Zabaras
2019	J	`jnl`	Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields. CoRR Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
2019	J	`jnl`	Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks. CoRR Nicholas Geneva, Nicholas Zabaras
2019	J	`jnl`	Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2019	J	`jnl`	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2019	J	`jnl`	Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks. J. Comput. Phys. Nicholas Geneva, Nicholas Zabaras
2019	J	`jnl`	Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion. J. Comput. Phys. Steven Atkinson, Nicholas Zabaras
2018	J	`jnl`	Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification. CoRR Yinhao Zhu, Nicholas Zabaras
2018	J	`jnl`	Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification. J. Comput. Phys. Yinhao Zhu, Nicholas Zabaras
2018	J	`jnl`	Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. CoRR Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
2018	J	`jnl`	Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. CoRR Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
2018	J	`jnl`	Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. Comput. Phys. Commun. Souvik Chakraborty, Nicholas Zabaras
2018	J	`jnl`	Parallel probabilistic graphical model approach for nonparametric Bayesian inference. J. Comput. Phys. Wonjung Lee, Nicholas Zabaras
2018	J	`jnl`	Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2018	J	`jnl`	Structured Bayesian Gaussian process latent variable model. CoRR Steven Atkinson, Nicholas Zabaras
2018	J	`jnl`	Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion. CoRR Steven Atkinson, Nicholas Zabaras
2017	J	`jnl`	Predictive coarse-graining. J. Comput. Phys. Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2017	J	`jnl`	Special Issue: Predictive multiscale materials modeling. J. Comput. Phys. Markos A. Katsoulakis, Nicholas Zabaras
2016	J	`jnl`	A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. J. Comput. Phys. Louis Ellam, Nicholas Zabaras, Mark A. Girolami
2016	J	`jnl`	Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory. J. Comput. Phys. Manuel Aldegunde, James R. Kermode, Nicholas Zabaras
2016	J	`jnl`	Manifold learning for the emulation of spatial fields from computational models. J. Comput. Phys. Wei W. Xing, Vasileios Triantafyllidis, Akeel A. Shah, Prasanth B. Nair, Nicholas Zabaras
2016	J	`jnl`	Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions. J. Comput. Phys. Manuel Aldegunde, Nicholas Zabaras, Jesper Kristensen
2016	J	`jnl`	Special Issue: Big data and predictive computational modeling. J. Comput. Phys. Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami
2015	J	`jnl`	Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference. J. Comput. Phys. Peng Chen, Nicholas Zabaras, Ilias Bilionis
2014	J	`jnl`	A probabilistic graphical model based stochastic input model construction. J. Comput. Phys. Jiang Wan, Nicholas Zabaras
2014	J	`jnl`	Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. Comput. Phys. Commun. Jesper Kristensen, Nicholas Zabaras
2013	J	`jnl`	A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media. J. Comput. Phys. Peng Chen, Nicholas Zabaras
2013	J	`jnl`	A probabilistic graphical model approach to stochastic multiscale partial differential equations. J. Comput. Phys. Jiang Wan, Nicholas Zabaras
2013	J	`jnl`	Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. J. Comput. Phys. Ilias Bilionis, Nicholas Zabaras, Bledar A. Konomi, Guang Lin
2012	J	`jnl`	Multi-output local Gaussian process regression: Applications to uncertainty quantification. J. Comput. Phys. Ilias Bilionis, Nicholas Zabaras
2012	J	`jnl`	Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification. SIAM J. Sci. Comput. Ilias Bilionis, Nicholas Zabaras
2011	J	`jnl`	A stochastic mixed finite element heterogeneous multiscale method for flow in porous media. J. Comput. Phys. Xiang Ma, Nicholas Zabaras
2011	J	`jnl`	Kernel principal component analysis for stochastic input model generation. J. Comput. Phys. Xiang Ma, Nicholas Zabaras
2010	J	`jnl`	An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations. J. Comput. Phys. Xiang Ma, Nicholas Zabaras
2009	J	`jnl`	A statistical learning approach for the design of polycrystalline materials. Stat. Anal. Data Min. Veera Sundararaghavan, Nicholas Zabaras
2009	J	`jnl`	A stochastic multiscale framework for modeling flow through random heterogeneous porous media. J. Comput. Phys. Baskar Ganapathysubramanian, Nicholas Zabaras
2009	J	`jnl`	An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J. Comput. Phys. Xiang Ma, Nicholas Zabaras
2008	J	`jnl`	A non-linear dimension reduction methodology for generating data-driven stochastic input models. J. Comput. Phys. Baskar Ganapathysubramanian, Nicholas Zabaras
2008	J	`jnl`	A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach. J. Comput. Phys. Nicholas Zabaras, Baskar Ganapathysubramanian
2008	J	`jnl`	A stabilized stochastic finite element second-order projection method for modeling natural convection in random porous media. J. Comput. Phys. Xiang Ma, Nicholas Zabaras
2007	J	`jnl`	A level set simulation of dendritic solidification of multi-component alloys. J. Comput. Phys. Lijian Tan, Nicholas Zabaras
2007	J	`jnl`	An Information-Theoretic Approach to Stochastic Materials Modeling. Comput. Sci. Eng. Nicholas Zabaras, Sethuraman Sankaran
2007	J	`jnl`	Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method. J. Comput. Phys. Baskar Ganapathysubramanian, Nicholas Zabaras
2007	J	`jnl`	Modeling the growth and interaction of multiple dendrites in solidification using a level set method. J. Comput. Phys. Lijian Tan, Nicholas Zabaras
2007	J	`jnl`	Multiscale modeling of alloy solidification using a database approach. J. Comput. Phys. Lijian Tan, Nicholas Zabaras
2007	J	`jnl`	Sparse grid collocation schemes for stochastic natural convection problems. J. Comput. Phys. Baskar Ganapathysubramanian, Nicholas Zabaras
2006	J	`jnl`	A stochastic variational multiscale method for diffusion in heterogeneous random media. J. Comput. Phys. Badrinarayanan Velamur Asokan, Nicholas Zabaras
2006	J	`jnl`	Modelling dendritic solidification with melt convection using the extended finite element method. J. Comput. Phys. Nicholas Zabaras, Baskar Ganapathysubramanian, Lijian Tan
1999	J	`jnl`	Using Objects to Model Finite Deformation Plasticity. Eng. Comput. Nicholas Zabaras, Akkaram Srikanth