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ORCIDNicholas Zabaras
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2020 – today
- 2022
[j43]Yingzhi Xia
, Nicholas Zabaras:
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. J. Comput. Phys. 455: 111008 (2022)- [j42]Nicholas Geneva, Nicholas Zabaras:
Transformers for modeling physical systems. Neural Networks 146: 272-289 (2022) - 2021
- [j41]Govinda Anantha Padmanabha, Nicholas Zabaras:
Solving inverse problems using conditional invertible neural networks. J. Comput. Phys. 433: 110194 (2021) - [i17]Yingzhi Xia, Nicholas Zabaras:
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems. CoRR abs/2102.03169 (2021) - [i16]Govinda Anantha Padmanabha, Nicholas Zabaras:
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media. CoRR abs/2103.09056 (2021) - [i15]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 abs/2108.10163 (2021) - [i14]Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky:
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties. CoRR abs/2110.12367 (2021) - 2020
- [j40]Nicholas Geneva, Nicholas Zabaras:
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. J. Comput. Phys. 403 (2020) - [i13]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR abs/2002.10148 (2020) - [i12]Nicholas Geneva, Nicholas Zabaras:
Multi-fidelity Generative Deep Learning Turbulent Flows. CoRR abs/2006.04731 (2020) - [i11]Govinda Anantha Padmanabha, Nicholas Zabaras:
Solving inverse problems using conditional invertible neural networks. CoRR abs/2007.15849 (2020) - [i10]Nicholas Geneva, Nicholas Zabaras:
Transformers for Modeling Physical Systems. CoRR abs/2010.03957 (2020)
2010 – 2019
- 2019
- [j39]Nicholas Geneva, Nicholas Zabaras:
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks. J. Comput. Phys. 383: 125-147 (2019) - [j38]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: 166-195 (2019) - [j37]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: 56-81 (2019) - [i9]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 abs/1901.06314 (2019) - [i8]Nicholas Geneva, Nicholas Zabaras:
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks. CoRR abs/1906.05747 (2019) - [i7]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 abs/1906.11828 (2019) - 2018
- [j36]Souvik Chakraborty, Nicholas Zabaras:
Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems. Comput. Phys. Commun. 230: 70-88 (2018) - [j35]Yinhao Zhu, Nicholas Zabaras:
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification. J. Comput. Phys. 366: 415-447 (2018) - [j34]Wonjung Lee, Nicholas Zabaras:
Parallel probabilistic graphical model approach for nonparametric Bayesian inference. J. Comput. Phys. 372: 546-563 (2018) - [i6]Yinhao Zhu, Nicholas Zabaras:
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification. CoRR abs/1801.06879 (2018) - [i5]Steven Atkinson, Nicholas Zabaras:
Structured Bayesian Gaussian process latent variable model. CoRR abs/1805.08665 (2018) - [i4]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 abs/1807.00882 (2018) - [i3]Steven Atkinson, Nicholas Zabaras:
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion. CoRR abs/1807.04302 (2018) - [i2]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR abs/1809.06913 (2018) - [i1]Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu:
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. CoRR abs/1812.09444 (2018) - 2017
- [j33]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Predictive coarse-graining. J. Comput. Phys. 333: 49-77 (2017) - [j32]Markos A. Katsoulakis, Nicholas Zabaras:
Special Issue: Predictive multiscale materials modeling. J. Comput. Phys. 338: 18-20 (2017) - 2016
- [j31]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: 173-195 (2016) - [j30]Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami:
Special Issue: Big data and predictive computational modeling. J. Comput. Phys. 321: 1252-1254 (2016) - [j29]Manuel Aldegunde, Nicholas Zabaras, Jesper Kristensen:
Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions. J. Comput. Phys. 323: 17-44 (2016) - [j28]Louis Ellam, Nicholas Zabaras, Mark A. Girolami:
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. J. Comput. Phys. 326: 115-140 (2016) - [j27]Wei W. Xing, Vasileios Triantafyllidis, Akeel A. Shah, Prasanth B. Nair, Nicholas Zabaras:
Manifold learning for the emulation of spatial fields from computational models. J. Comput. Phys. 326: 666-690 (2016) - 2015
- [j26]Peng Chen, Nicholas Zabaras, Ilias Bilionis:
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference. J. Comput. Phys. 284: 291-333 (2015) - 2014
- [j25]Jesper Kristensen, Nicholas Zabaras:
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. Comput. Phys. Commun. 185(11): 2885-2892 (2014) - [j24]Jiang Wan, Nicholas Zabaras:
A probabilistic graphical model based stochastic input model construction. J. Comput. Phys. 272: 664-685 (2014) - 2013
- [j23]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: 212-239 (2013) - [j22]Jiang Wan, Nicholas Zabaras:
A probabilistic graphical model approach to stochastic multiscale partial differential equations. J. Comput. Phys. 250: 477-510 (2013) - [j21]Peng Chen, Nicholas Zabaras:
A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media. J. Comput. Phys. 250: 616-643 (2013) - 2012
- [j20]Ilias Bilionis, Nicholas Zabaras:
Multi-output local Gaussian process regression: Applications to uncertainty quantification. J. Comput. Phys. 231(17): 5718-5746 (2012) - [j19]Ilias Bilionis, Nicholas Zabaras:
Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification. SIAM J. Sci. Comput. 34(6) (2012) - 2011
- [j18]Xiang Ma, Nicholas Zabaras:
A stochastic mixed finite element heterogeneous multiscale method for flow in porous media. J. Comput. Phys. 230(12): 4696-4722 (2011) - [j17]Xiang Ma, Nicholas Zabaras:
Kernel principal component analysis for stochastic input model generation. J. Comput. Phys. 230(19): 7311-7331 (2011) - 2010
- [j16]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): 3884-3915 (2010)
2000 – 2009
- 2009
- [j15]Baskar Ganapathysubramanian, Nicholas Zabaras:
A stochastic multiscale framework for modeling flow through random heterogeneous porous media. J. Comput. Phys. 228(2): 591-618 (2009) - [j14]Xiang Ma, Nicholas Zabaras:
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J. Comput. Phys. 228(8): 3084-3113 (2009) - [j13]Veera Sundararaghavan, Nicholas Zabaras:
A statistical learning approach for the design of polycrystalline materials. Stat. Anal. Data Min. 1(5): 306-321 (2009) - 2008
- [j12]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): 4697-4735 (2008) - [j11]Baskar Ganapathysubramanian, Nicholas Zabaras:
A non-linear dimension reduction methodology for generating data-driven stochastic input models. J. Comput. Phys. 227(13): 6612-6637 (2008) - [j10]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): 8448-8471 (2008) - 2007
- [j9]Nicholas Zabaras, Sethuraman Sankaran:
An Information-Theoretic Approach to Stochastic Materials Modeling. Comput. Sci. Eng. 9(2): 30-39 (2007) - [j8]Lijian Tan, Nicholas Zabaras:
A level set simulation of dendritic solidification of multi-component alloys. J. Comput. Phys. 221(1): 9-40 (2007) - [j7]Baskar Ganapathysubramanian, Nicholas Zabaras:
Sparse grid collocation schemes for stochastic natural convection problems. J. Comput. Phys. 225(1): 652-685 (2007) - [j6]Lijian Tan, Nicholas Zabaras:
Modeling the growth and interaction of multiple dendrites in solidification using a level set method. J. Comput. Phys. 226(1): 131-155 (2007) - [j5]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): 326-353 (2007) - [j4]Lijian Tan, Nicholas Zabaras:
Multiscale modeling of alloy solidification using a database approach. J. Comput. Phys. 227(1): 728-754 (2007) - 2006
- [j3]Nicholas Zabaras, Baskar Ganapathysubramanian, Lijian Tan:
Modelling dendritic solidification with melt convection using the extended finite element method. J. Comput. Phys. 218(1): 200-227 (2006) - [j2]Badrinarayanan Velamur Asokan, Nicholas Zabaras:
A stochastic variational multiscale method for diffusion in heterogeneous random media. J. Comput. Phys. 218(2): 654-676 (2006)
1990 – 1999
- 1999
- [j1]Nicholas Zabaras, Akkaram Srikanth:
Using Objects to Model Finite Deformation Plasticity. Eng. Comput. 15(1): 37-60 (1999)
Coauthor Index
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