![](https://dblp1.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp1.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp1.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp1.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp1.uni-trier.de/img/search.dark.16x16.png)
default search action
Nicholas Zabaras
Person information
Refine list
![note](https://dblp1.uni-trier.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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
![](https://dblp1.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-24 22:59 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint