Nicholas Zabaras

Publication Activity (10 Years)

Years Active: 1999-2022
Publications (10 Years): 34

Top Topics

Inverse Problems

Variational Bayesian Inference

Computational Modeling

Top Venues

J. Comput. Phys.

Comput. Phys. Commun.

Neural Networks

Publications

Yingzhi Xia, Nicholas Zabaras
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. J. Comput. Phys. 455 (2022)
Nicholas Geneva, Nicholas Zabaras
Transformers for modeling physical systems. Neural Networks 146 (2022)
Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. CoRR (2021)
Yingzhi Xia, Nicholas Zabaras
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. CoRR (2021)
Govinda Anantha Padmanabha, Nicholas Zabaras
Solving inverse problems using conditional invertible neural networks. J. Comput. Phys. 433 (2021)
Govinda Anantha Padmanabha, Nicholas Zabaras
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media. CoRR (2021)
Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Valeria Andreoli, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning. CoRR (2021)
Nicholas Geneva, Nicholas Zabaras
Transformers for Modeling Physical Systems. CoRR (2020)
Nicholas Geneva, Nicholas Zabaras
Multi-fidelity Generative Deep Learning Turbulent Flows. CoRR (2020)
Nicholas Geneva, Nicholas Zabaras
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. J. Comput. Phys. 403 (2020)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR (2020)
Govinda Anantha Padmanabha, Nicholas Zabaras
Solving inverse problems using conditional invertible neural networks. CoRR (2020)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394 (2019)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR (2019)
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields. CoRR (2019)
Steven Atkinson, Nicholas Zabaras
Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion. J. Comput. Phys. 383 (2019)
Nicholas Geneva, Nicholas Zabaras
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks. CoRR (2019)
Nicholas Geneva, Nicholas Zabaras
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks. J. Comput. Phys. 383 (2019)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR (2018)
Steven Atkinson, Nicholas Zabaras
Structured Bayesian Gaussian process latent variable model. CoRR (2018)
Yinhao Zhu, Nicholas Zabaras
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification. J. Comput. Phys. 366 (2018)
Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. CoRR (2018)
Wonjung Lee, Nicholas Zabaras
Parallel probabilistic graphical model approach for nonparametric Bayesian inference. J. Comput. Phys. 372 (2018)
Souvik Chakraborty, Nicholas Zabaras
Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. Comput. Phys. Commun. 230 (2018)
Yinhao Zhu, Nicholas Zabaras
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification. CoRR (2018)
Steven Atkinson, Nicholas Zabaras
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion. CoRR (2018)
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. CoRR (2018)
Markos A. Katsoulakis, Nicholas Zabaras
Special Issue: Predictive multiscale materials modeling. J. Comput. Phys. 338 (2017)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Predictive coarse-graining. J. Comput. Phys. 333 (2017)
Manuel Aldegunde, Nicholas Zabaras, Jesper Kristensen
Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions. J. Comput. Phys. 323 (2016)
Manuel Aldegunde, James R. Kermode, Nicholas Zabaras
Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory. J. Comput. Phys. 311 (2016)
Louis Ellam, Nicholas Zabaras, Mark A. Girolami
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. J. Comput. Phys. 326 (2016)
Wei W. Xing, Vasileios Triantafyllidis, Akeel A. Shah, P. B. Nair, Nicholas Zabaras
Manifold learning for the emulation of spatial fields from computational models. J. Comput. Phys. 326 (2016)
Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami
Special Issue: Big data and predictive computational modeling. J. Comput. Phys. 321 (2016)
Peng Chen, Nicholas Zabaras, Ilias Bilionis
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference. J. Comput. Phys. 284 (2015)
Jiang Wan, Nicholas Zabaras
A probabilistic graphical model based stochastic input model construction. J. Comput. Phys. 272 (2014)
Jesper Kristensen, Nicholas Zabaras
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. Comput. Phys. Commun. 185 (11) (2014)
Peng Chen, Nicholas Zabaras
A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media. J. Comput. Phys. 250 (2013)
Ilias Bilionis, Nicholas Zabaras, Bledar A. Konomi, Guang Lin
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. J. Comput. Phys. 241 (2013)
Jiang Wan, Nicholas Zabaras
A probabilistic graphical model approach to stochastic multiscale partial differential equations. J. Comput. Phys. 250 (2013)
Ilias Bilionis, Nicholas Zabaras
Multi-output local Gaussian process regression: Applications to uncertainty quantification. J. Comput. Phys. 231 (17) (2012)
Ilias Bilionis, Nicholas Zabaras
Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification. SIAM J. Sci. Comput. 34 (6) (2012)
Xiang Ma, Nicholas Zabaras
A stochastic mixed finite element heterogeneous multiscale method for flow in porous media. J. Comput. Phys. 230 (12) (2011)
Xiang Ma, Nicholas Zabaras
Kernel principal component analysis for stochastic input model generation. J. Comput. Phys. 230 (19) (2011)
Xiang Ma, Nicholas Zabaras
An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations. J. Comput. Phys. 229 (10) (2010)
Xiang Ma, Nicholas Zabaras
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J. Comput. Phys. 228 (8) (2009)
Veera Sundararaghavan, Nicholas Zabaras
A statistical learning approach for the design of polycrystalline materials. Stat. Anal. Data Min. 1 (5) (2009)
Baskar Ganapathysubramanian, Nicholas Zabaras
A stochastic multiscale framework for modeling flow through random heterogeneous porous media. J. Comput. Phys. 228 (2) (2009)
Nicholas Zabaras, Baskar Ganapathysubramanian
A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach. J. Comput. Phys. 227 (9) (2008)
Baskar Ganapathysubramanian, Nicholas Zabaras
A non-linear dimension reduction methodology for generating data-driven stochastic input models. J. Comput. Phys. 227 (13) (2008)
Xiang Ma, Nicholas Zabaras
A stabilized stochastic finite element second-order projection method for modeling natural convection in random porous media. J. Comput. Phys. 227 (18) (2008)
Lijian Tan, Nicholas Zabaras
Modeling the growth and interaction of multiple dendrites in solidification using a level set method. J. Comput. Phys. 226 (1) (2007)
Nicholas Zabaras, Sethuraman Sankaran
An Information-Theoretic Approach to Stochastic Materials Modeling. Comput. Sci. Eng. 9 (2) (2007)
Baskar Ganapathysubramanian, Nicholas Zabaras
Sparse grid collocation schemes for stochastic natural convection problems. J. Comput. Phys. 225 (1) (2007)
Baskar Ganapathysubramanian, Nicholas Zabaras
Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method. J. Comput. Phys. 226 (1) (2007)
Lijian Tan, Nicholas Zabaras
A level set simulation of dendritic solidification of multi-component alloys. J. Comput. Phys. 221 (1) (2007)
Lijian Tan, Nicholas Zabaras
Multiscale modeling of alloy solidification using a database approach. J. Comput. Phys. 227 (1) (2007)
Badrinarayanan Velamur Asokan, Nicholas Zabaras
A stochastic variational multiscale method for diffusion in heterogeneous random media. J. Comput. Phys. 218 (2) (2006)
Nicholas Zabaras, Baskar Ganapathysubramanian, Lijian Tan
Modelling dendritic solidification with melt convection using the extended finite element method. J. Comput. Phys. 218 (1) (2006)
Nicholas Zabaras, Akkaram Srikanth
Using Objects to Model Finite Deformation Plasticity. Eng. Comput. 15 (1) (1999)