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Nicholas Zabaras
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Publications
- 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) - 2020
- [j40]Nicholas Geneva, Nicholas Zabaras:
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. J. Comput. Phys. 403 (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) - 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) - [i8]Nicholas Geneva, Nicholas Zabaras:
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks. CoRR abs/1906.05747 (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) - [j34]Wonjung Lee, Nicholas Zabaras:
Parallel probabilistic graphical model approach for nonparametric Bayesian inference. J. Comput. Phys. 372: 546-563 (2018) - 2014
- [j24]Jiang Wan, Nicholas Zabaras:
A probabilistic graphical model based stochastic input model construction. J. Comput. Phys. 272: 664-685 (2014) - 2013
- [j22]Jiang Wan, Nicholas Zabaras:
A probabilistic graphical model approach to stochastic multiscale partial differential equations. J. Comput. Phys. 250: 477-510 (2013) - 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) - 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
- [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) - 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) - 1999
- [j1]Nicholas Zabaras, Akkaram Srikanth:
Using Objects to Model Finite Deformation Plasticity. Eng. Comput. 15(1): 37-60 (1999)
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