N. Zabaras

发表

Finite element solution of two‐dimensional inverse elastic problems using spatial smoothing

D. Schnur, N. Zabaras, 1990 .

An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations

Xiang Ma, Nicholas Zabaras, N. Zabaras, 2009, J. Comput. Phys..

Using stochastic analysis to capture unstable equilibrium in natural convection

N. Zabaras, B. V. Asokan, 2005 .

An analysis of inverse heat transfer problems with phase changes using an integral method

S. Mukherjee, N. Zabaras, O. Richmond, 1988 .

An inverse method for determining elastic material properties and a material interface

D. Schnur, N. Zabaras, 1992 .

Uncertainty propagation in finite deformations––A spectral stochastic Lagrangian approach

N. Zabaras, Swagato Acharjee, 2006 .

A continuum sensitivity method for the design of multi-stage metal forming processes

N. Zabaras, S. Ganapathysubramanian, Qing Li, 2003 .

Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective

N. Zabaras, I. Bilionis, I. Bilionis, 2013 .

Front tracking thermomechanical model for hypoelastic-viscoplastic behavior in a solidifying body

Nicholas Zabaras, Yimin Ruan, O. Richmond, 1990 .

A Bayesian approach to multiscale inverse problems using the sequential Monte Carlo method

Nicholas Zabaras, N. Zabaras, Jiang Wan, 2011 .

AN ADJOINT METHOD FOR THE INVERSE DESIGN OF SOLIDIFICATION PROCESSES WITH NATURAL CONVECTION

Nicholas Zabaras, N. Zabaras, G. Yang, 1998 .

An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method

Nicholas Zabaras, N. Zabaras, Xiang Ma, 2009 .

A level set simulation of dendritic solidification with combined features of front-tracking and fixed-domain methods

N. Zabaras, Lijian Tan, 2006 .

Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder

N. Zabaras, Navid Shervani-Tabar, 2020, 2009.13878.

Viscous damping approximation of laminated anisotropic composite plates using the finite element method

N. Zabaras, T. Pervez, 1990 .

A dynamic material library for the representation of single-phase polyhedral microstructures

N. Zabaras, V. Sundararaghavan, 2004 .

Design across length scales: a reduced-order model of polycrystal plasticity for the control of microstructure-sensitive material properties

N. Zabaras, S. Ganapathysubramanian, 2004 .

Linear analysis of texture-property relationships using process-based representations of Rodrigues space

N. Zabaras, V. Sundararaghavan, 2007 .

Microstructure model reduction and uncertainty quantification in multiscale deformation processes

N. Zabaras, B. Kouchmeshky, 2010 .

The effect of multiple sources of uncertainty on the convex hull of material properties of polycrystals

N. Zabaras, B. Kouchmeshky, Babak Kouchmeshky, 2009 .

Design of microstructure-sensitive properties in elasto-viscoplastic polycrystals using multi-scale homogenization

N. Zabaras, V. Sundararaghavan, 2006 .

Using Bayesian statistics in the estimation of heat source in radiation

Nicholas Zabaras, N. Zabaras, Jingbo Wang, 2005 .

Variational multiscale stabilized FEM formulations for transport equations: stochastic advection-

Nicholas Zabaras, N. Zabaras, V. A. B. Narayanan, 2004 .

A maximum entropy approach for property prediction of random microstructures

Nicholas Zabaras, Sethuraman Sankaran, N. Zabaras, 2006 .

Finite Element Analysis of Some Inverse Elasticity Problems

Kim Stelson, Nicholas Zabaras, Antoinette M. Maniatty, 1989 .

Investigation of regularization parameters and error estimating in inverse elasticity problems

Nicholas Zabaras, Antoinette M. Maniatty, N. Zabaras, 1994 .

A seamless approach towards stochastic modeling: Sparse grid collocation and data driven input models

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2008 .

Modeling convection in solidification processes using stabilized finite element techniques Deep

N. Zabaras, 2005 .

Numerical study of macrosegregation in Aluminum alloys solidifying on uneven surfaces

N. Zabaras, D. Samanta, Deep Samanta, 2005 .

Modelling convection in solidification processes using stabilized finite element techniques

Nicholas Zabaras, N. Zabaras, D. Samanta, 2005 .

A stabilized volume‐averaging finite element method for flow in porous media and binary alloy solidification processes

Nicholas Zabaras, N. Zabaras, D. Samanta, 2004 .

Computing property variability of polycrystals induced by grain size and orientation uncertainties

Nicholas Zabaras, Sethuraman Sankaran, N. Zabaras, 2007 .

A markov random field model of contamination source identification in porous media flow

Nicholas Zabaras, N. Zabaras, Jingbo Wang, 2006 .

Hierarchical Bayesian models for inverse problems in heat conduction

Nicholas Zabaras, N. Zabaras, Jingbo Wang, 2005 .

A Computational Statistics Approach to Stochastic Inverse Problems and Uncertainty Quantication in Heat Transfer

N. Zabaras, John Y. A. Wang, 2004 .

Design of Two-Dimensional Stefan Processes with Desired Freezing Front Motions

Owen Richmond, Nicholas Zabaras, Yimin Ruan, 1992 .

Inverse finite element techniques for the analysis of solidification processes

Nicholas Zabaras, N. Zabaras, 1990 .

Moving and deforming finite‐element simulation of two‐dimensional Stefan problems

Nicholas Zabaras, Yimin Ruan, N. Zabaras, 1990 .

A deforming finite element method analysis of inverse stefan problems

Nicholas Zabaras, Yimin Ruan, N. Zabaras, 1989 .

An analysis of solidification problems by the boundary element method

Nicholas Zabaras, Subrata Mukherjee, S. Mukherjee, 1987 .

Transient dynamic and damping analysis of laminated anisotropic plates using a refined plate theory

Tasneem Pervez, Nicholas Zabaras, N. Zabaras, 1992 .

Inverse Problems in Heat Transfer

Nicholas Zabaras, N. Zabaras, 2009 .

An Efficient Markov Chain Monte Carlo Simulation of a Stochastic Inverse Radiation Problem

N. Zabaras, Jingbo Wang, 2004 .

A functional optimization approach to an inverse magneto- convection problem

N. Zabaras, R. Sampath, 2001 .

Inverse design of directional solidification processes in the presence of a strong external magnetic field

N. Zabaras, R. Sampath, 2001 .

Stochastic inverse heat conduction using a spectral approach

N. Zabaras, V. A. B. Narayanan, 2004 .

Investigating variability of fatigue indicator parameters of two-phase nickel-based superalloy microstructures

N. Zabaras, B. Wen, 2012 .

Adjoint Methods for Inverse Free Convection Problems with Application to Solidification Processes

N. Zabaras, 1998 .

Adaptive LocallyWeighted ProjectionRegressionMethod for Uncertainty Quantification

N. Zabaras, Peng Chen, 2013 .

An updated Lagrangian + nite element sensitivity analysis of large deformations using quadrilateral elements Akkaram Srikanth and

N. Zabaras, 2001 .

On the performance of two tangent operators for finite element analysis of large deformation inelastic problems

N. Zabaras, A. Arif, 1992 .

Thermal Response Variability of Random Polycrystalline Microstructures

N. Zabaras, B. Wen, Zheng Li, 2011 .

Bayesian Uncertainty Propagation Using Gaussian Processes

Ilias Bilionis, Nicholas Zabaras, N. Zabaras, 2015 .

Adaptive Simulation Selection for the Discovery of the Ground State Line of Binary Alloys with a Limited Computational Budget

Ilias Bilionis, Nicholas Zabaras, Jesper Kristensen, 2017 .

Numerical study of convection in the directional solidification of a binary alloy driven by the combined action of Buoyancy, surface tension, and electromagnetic forces

N. Zabaras, R. Sampath, 2001 .

Developing the Next Generation Scalable Exascale Uncertainty Quantification Methods

T. Manteuffel, I. Kevrekidis, N. Zabaras, 2012 .

Modeling multiscale diffusion processes in random heterogeneous media

N. Zabaras, Basked Ganapathysubramanian, 2008 .

Chapter 1 An object-oriented approach to the nite element modeling and design of material processes

N. Zabaras, 2000 .

Stochastic modeling of multiscale and multiphysics problems

Dongbin Xiu, Nicholas Zabaras, N. Zabaras, 2008 .

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

N. Zabaras, D. Tartakovsky, Zitong Zhou, 2021, Water Resources Research.

Using magnetic field gradients to control the directional solidification of alloys and the growth of single crystals

N. Zabaras, B. Ganapathysubramanian, 2004 .

Modeling and Simulation of High-Dimensional Stochastic Multiscale PDE Systems

N. Zabaras, G. Lin, Y. Kevrekidis, 2011 .

An Information-Theoretic Multiscale Framework With Applications to Polycrystalline Materials

N. Zabaras, 2009 .

A Phase-Tracking Snow Micro-Structure Model

N. Zabaras, A. Slaughter, 2012 .

Modeling the response of HCP polycrystals deforming by slip and twinning using a finite element representation of the orientation space

N. Zabaras, B. Kouchmeshky, 2009 .

A NUMERICAL AND EXPERIMENTAL STUDY OF QUENCHING OF CIRCULAR CYLINDERS

S. Mukherjee, N. Zabaras, W. R. Arthur, 1987 .

Transformers for Modeling Physical Systems

N. Zabaras, N. Geneva, 2020, Neural Networks.

Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media pdf

Jichun Wu, Nicholas Zabaras, Shaoxing Mo, 2018, Water Resources Research.

Mixture Models and Expectation- Maximization (EM)

Nicholas Zabaras, N. Zabaras, 2014 .

Manifold learning for the emulation of spatial fields from computational models

A.A. Shah, W.W. Xing, V. Triantafyllidis, 2016, J. Comput. Phys..

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference

Peng Chen, Ilias Bilionis, Nicholas Zabaras, 2015, J. Comput. Phys..

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

Ilias Bilionis, Guang Lin, Bledar A. Konomi, 2013, J. Comput. Phys..

Multi-output local Gaussian process regression: Applications to uncertainty quantification

Ilias Bilionis, Nicholas Zabaras, N. Zabaras, 2012, J. Comput. Phys..

Control of solidification of non-conducting materials using tailored magnetic fields

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2005 .

Melt flow control using magnetic fields and magnetic field gradients

N. Zabaras, B. Ganapathysubramanian, 2004 .

The Adjoint Method for an Inverse Design Problem in the Directional Solidification of Binary Alloys

Nicholas Zabaras, N. Zabaras, G. Yang, 1998 .

A functional optimization formulation and implementation of an inverse natural convection problem

Nicholas Zabaras, N. Zabaras, G. Yang, 1997 .

Computationally Efficient Variational Approximations for Bayesian Inverse Problems

N. Zabaras, P. Tsilifis, Ilias Bilionis, 2016 .

A probabilistic graphical model based stochastic input model construction

Jiang Wan, Nicholas Zabaras, N. Zabaras, 2014, J. Comput. Phys..

Kernel principal component analysis for stochastic input model generation

Xiang Ma, Nicholas Zabaras, N. Zabaras, 2010, J. Comput. Phys..

Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields

N. Zabaras, Jichun Wu, Xiaoqing Shi, 2019, ArXiv.

Simulating Interface Growth and Defect Generation in CZT – Simulation State of the Art and Known Gaps

Fei Gao, Eric J. Bylaska, Charles H. Henager, 2012 .

Modeling the growth and interaction of multiple dendrites in solidification using a level set method

Lijian Tan, Nicholas Zabaras, N. Zabaras, 2007, J. Comput. Phys..

A virtual environment for the interrogation of 3D polycrystalline microstructures including grain size effects

Wei Li, Nicholas Zabaras, N. Zabaras, 2009 .

A COMPUTATIONAL MODEL FOR THE FINITE ELEMENT ANALYSIS OF THERMOPLASTICITY COUPLED WITH DUCTILE DAMAGE AT FINITE STRAINS

Nicholas Zabaras, Akkaram Srikanth, N. Zabaras, 1999 .

Uncertainty quantification for multiscale disk forging of polycrystal materials using probabilistic graphical model techniques

Peng Chen, Nicholas Zabaras, N. Zabaras, 2014 .

Relative entropy as model selection tool in cluster expansions

Ilias Bilionis, Jesper Kristensen, Nicholas Zabaras, 2013 .

A Continuum Lagrangian Sensitivity Analysis for Metal Forming Processes with Applications to Die Design Problems

Nicholas Zabaras, Akkaram Srikanth, W. G. Frazier, 2000 .

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data pdf

Paris Perdikaris, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2019, J. Comput. Phys..

An object-oriented programming approach to the Lagrangian FEM analysis of large inelastic deformations and metal-forming processes

Nicholas Zabaras, Akkaram Srikanth, N. Zabaras, 1999 .

A family of integration algorithms for constitutive equations in finite deformation elasto‐viscoplasticity

Abul Fazal M. Arif, Nicholas Zabaras, N. Zabaras, 1992 .

Predictive Collective Variable Discovery with Deep Bayesian Models

Markus Schöberl, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2018, The Journal of chemical physics.

Solving inverse problems using conditional invertible neural networks

Nicholas Zabaras, Govinda Anantha Padmanabha, N. Zabaras, 2020, J. Comput. Phys..

A Bayesian inference approach to the inverse heat conduction problem

Nicholas Zabaras, N. Zabaras, Jingbo Wang, 2004 .

A coupled thermomechanical, thermal transport and segregation analysis of the solidification of aluminum alloys on molds of uneven topographies

Nicholas Zabaras, N. Zabaras, Deep Samanta, 2005 .

Solidification and Macrosegregation in Aluminum Alloys on Uneven Surfaces

N. Zabaras, Deep Samanta, 2003 .

Control of macrosegregation during the solidification of alloys using magnetic fields

N. Zabaras, Deep Samanta, 2006 .

A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in deformation processes

Nicholas Zabaras, Swagato Acharjee, N. Zabaras, 2007 .

The continuum sensitivity method for the computational design of three-dimensional deformation processes

Nicholas Zabaras, Swagato Acharjee, N. Zabaras, 2006 .

Modeling the thermoelastic¿viscoplastic response of polycrystals using a continuum representation over the orientation space

N. Zabaras, S. Ganapathysubramanian, 2004 .

Deformation process design for control of microstructure in the presence of dynamic recrystallization and grain growth mechanisms

Nicholas Zabaras, Shankar Ganapathysubramanian, N. Zabaras, 2004 .

Computational design of deformation processes for materials with ductile damage

Nicholas Zabaras, Shankar Ganapathysubramanian, N. Zabaras, 2003 .

A continuum sensitivity method for finite thermo‐inelastic deformations with applications to the design of hot forming processes

Nicholas Zabaras, Shankar Ganapathysubramanian, N. Zabaras, 2002 .

An updated Lagrangian finite element sensitivity analysis of large deformations using quadrilateral elements

Nicholas Zabaras, Akkaram Srikanth, N. Zabaras, 2001 .

Shape optimization and preform design in metal forming processes

Nicholas Zabaras, Akkaram Srikanth, N. Zabaras, 2000 .

An Object-Oriented Approach to the Finite Element Modeling and Design of Material Processes

Nicholas Zabaras, N. Zabaras, 2000 .

Using Objects to Model Finite Deformation Plasticity

Nicholas Zabaras, Akkaram Srikanth, N. Zabaras, 1999, Engineering with Computers.

A sensitivity analysis for the optimal design of metal-forming processes

Nicholas Zabaras, Sundararajan Badrinarayanan, N. Zabaras, 1996 .

A Two-Dimensional FEM Code For The Analysis Of Large Deformations Of Hyperelastic Viscoplastic Solid

N. Zabaras, S. Badrinarayanan, 1993 .

On the control of solidification using magnetic fields and magnetic field gradients

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2005 .

Predicting Low Thermal Conductivity SiGe Nanowires with a Novel Cluster Expansion Approach

N. Zabaras, Jesper Kristensen, 2014 .

A multiscale approach for model reduction of random microstructures

Bin Wen, Nicholas Zabaras, N. Zabaras, 2012 .

Computing mechanical response variability of polycrystalline microstructures through dimensionality reduction techniques

Bin Wen, Nicholas Zabaras, Zheng Li, 2010 .

A Multi-length Scale Continuum Sensitivity Analysis for the Control of Texture-dependent Properties in Deformation Processing

N. Zabaras, V. Sundararaghavan, 2017 .

A multi-length scale sensitivity analysis for the control of texture-dependent properties in deformation processing

Veera Sundararaghavan, Nicholas Zabaras, N. Zabaras, 2008 .

Classification and reconstruction of three-dimensional microstructures using support vector machines

Nicholas Zabaras, Veeraraghavan Sundararaghavan, N. Zabaras, 2005 .

An object oriented implementation of a front tracking finite element method for directional solidification processes

Rajiv Sampath, Nicholas Zabaras, N. Zabaras, 1999 .

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Nicholas Geneva, Nicholas Zabaras, N. Zabaras, 2018, J. Comput. Phys..

Parallel probabilistic graphical model approach for nonparametric Bayesian inference

Nicholas Zabaras, Wonjung Lee, N. Zabaras, 2018, J. Comput. Phys..

Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach

N. Zabaras, N. Zabaras, 2010 .

Sparse grid collocation schemes for stochastic natural convection problems

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2007, J. Comput. Phys..

Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks pdf

Nicholas Geneva, Nicholas Zabaras, N. Zabaras, 2019, J. Comput. Phys..

Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification pdf

Nicholas Zabaras, Yinhao Zhu, N. Zabaras, 2018, J. Comput. Phys..

Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions

Jesper Kristensen, Manuel Aldegunde, Nicholas Zabaras, 2016, J. Comput. Phys..

Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method

Jesper Kristensen, Nicholas Zabaras, N. Zabaras, 2014, Comput. Phys. Commun..

Predictive coarse-graining

Markus Schöberl, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2016, J. Comput. Phys..

SPATIAL STATISTICS MODELS FOR STOCHASTIC INVERSE PROBLEMS IN HEAT CONDUCTION

N. Zabaras, John Y. A. Wang, 2004 .

SOLIDIFICATION PROBLEMS BY THE BOUNDARY ELEMENT METHOD

Nicholas Zabaras, Subrata Mukherjee, S. Mukherjee, 1994 .

On the solution of an ill‐posed design solidification problem using minimization techniques in finite‐ and infinite‐dimensional function spaces

Nicholas Zabaras, Shinill Kang, N. Zabaras, 1993 .

An inverse finite‐element technique to determine the change of phase interface location in two‐dimensional melting problems

Nicholas Zabaras, Yimin Ruan, N. Zabaras, 1991 .

Spatially regularized solution of inverse elasticity problems using the BEM

D. Schnur, N. Zabaras, V. Morellas, 1989 .

AN ANALYSIS OF TWO-DIMENSIONAL LINEAR INVERSE HEAT TRANSFER PROBLEMS USING AN INTEGRAL METHOD

N. Zabaras, Joshua C. Liu, 1988 .

Control of the freezing interface motion in two‐dimensional solidification processes using the adjoint method

Nicholas Zabaras, Shinill Kang, N. Zabaras, 1995 .

A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2008, J. Comput. Phys..

Multi-fidelity Generative Deep Learning Turbulent Flows pdf

Nicholas Geneva, Nicholas Zabaras, N. Zabaras, 2020, Foundations of Data Science.

A stochastic multiscale framework for modeling flow through random heterogeneous porous media

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2009, J. Comput. Phys..

A stabilized stochastic finite element second-order projection method for modeling natural convection in random porous media

Xiang Ma, Nicholas Zabaras, N. Zabaras, 2008, J. Comput. Phys..

Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2007, J. Comput. Phys..

A stochastic variational multiscale method for diffusion in heterogeneous random media

Nicholas Zabaras, Badrinarayanan Velamur Asokan, N. Zabaras, 2006, J. Comput. Phys..

A concurrent model reduction approach on spatial and random domains for the solution of stochastic PDEs

Nicholas Zabaras, Swagato Acharjee, N. Zabaras, 2006 .

FEM FORMULATIONS FOR STOCHASTIC ADVECTION-DIFFUSION EQUATIONS

N. Zabaras, V. A. B. Narayanan, 2004 .

A Variational Multiscale Stabilized Finite Element Method for Stochastic Advection-Diusion and Stochastic Incompress-

N. Zabaras, V. A. B. Narayanan, 2004 .

A spectral stochastic finite element implementation of probabilistic advective-diffusive transport with stabilization based on multi-scale phenomena

N. Zabaras, V. A. B. Narayanan, 2003 .

An object‐oriented framework for the implementation of adjoint techniques in the design and control of complex continuum systems

Rajiv Sampath, Nicholas Zabaras, N. Zabaras, 2000 .

Control of the freezing interface morphology in solidification processes in the presence of natural convection

Nicholas Zabaras, N. Zabaras, T. H. Nguyen, 1995 .

A least‐squares front‐tracking finite element method analysis of phase change with natural convection

Nicholas Zabaras, N. Zabaras, David J. McDaniel, 1994 .

A statistical learning approach for the design of polycrystalline materials

Veera Sundararaghavan, Nicholas Zabaras, N. Zabaras, 2009, Stat. Anal. Data Min..

Pro-ML IDeAS: A Probabilistic Framework for Explicit Inverse Design using Invertible Neural Network

Liping Wang, Piyush Pandita, Genghis Khan, 2020, AIAA Scitech 2021 Forum.

A non-linear dimension reduction methodology for generating data-driven stochastic input models

Baskar Ganapathysubramanian, Nicholas Zabaras, N. Zabaras, 2008, J. Comput. Phys..

Embedded-physics machine learning for coarse-graining and collective variable discovery without data pdf

Markus Schöberl, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2020, ArXiv.

A level set simulation of dendritic solidification of multi-component alloys

Lijian Tan, Nicholas Zabaras, N. Zabaras, 2007, J. Comput. Phys..

An Image-Based Method for Modeling the Elasto-Plastic Behavior of Polycrystalline Microstructures Based on the Fast Fourier Transform

N. Zabaras, B. Wen, 2012 .

A stochastic optimization approach to coarse-graining using a relative-entropy framework.

Ilias Bilionis, Nicholas Zabaras, N. Zabaras, 2013, The Journal of chemical physics.

Modelling dendritic solidification with melt convection using the extended finite element method

Baskar Ganapathysubramanian, Lijian Tan, Nicholas Zabaras, 2006, J. Comput. Phys..

Structured Bayesian Gaussian process latent variable model pdf

Nicholas Zabaras, Steven Atkinson, N. Zabaras, 2018, ArXiv.

Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification pdf

Jichun Wu, Nicholas Zabaras, Shaoxing Mo, 2018, Water Resources Research.

A stochastic mixed finite element heterogeneous multiscale method for flow in porous media

Xiang Ma, Nicholas Zabaras, N. Zabaras, 2011, J. Comput. Phys..

An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations

Xiang Ma, Nicholas Zabaras, N. Zabaras, 2010, J. Comput. Phys..

Multiscale modeling of alloy solidification using a database approach

Lijian Tan, Nicholas Zabaras, N. Zabaras, 2007, J. Comput. Phys..

On the synergy between texture classification and deformation process sequence selection for the control of texture-dependent properties

Nicholas Zabaras, N. Zabaras, V. Sundararaghavan, 2005 .

A proper orthogonal decomposition approach to microstructure model reduction in Rodrigues space with applications to optimal control of microstructure-sensitive properties

Nicholas Zabaras, Swagato Acharjee, N. Zabaras, 2003 .

Finite element analysis of progressive failure in laminated composite plates

Nicholas Zabaras, S. Tolson, N. Zabaras, 1991 .

A Bayesian approach to multiscale inverse problems with on-the-fly scale determination

Mark A. Girolami, Nicholas Zabaras, Louis Ellam, 2016, J. Comput. Phys..

Design and object-oriented implementation of a preconditioned-stabilized incompressible NavierStokes

Rajiv Sampath, Nicholas Zabaras, N. Zabaras, 2000 .

MODELING OF MICROSTRUCTURE EVOLUTION IN THE SOLIDIFICATION OF MULTI-COMPONENT ALLOYS USING LEVEL SET METHODS

N. Zabaras, Lijian Tan, 2005 .

A thermomechanical study of the effects of mold topography on the solidification of Aluminum alloys

Lijian Tan, Nicholas Zabaras, N. Zabaras, 2005 .

A Novel Cluster Expansion Approach for Finite and Infinite Systems of Arbitrary Shapes

N. Zabaras, Jesper Kristensen, 2014 .

Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory

Manuel Aldegunde, Nicholas Zabaras, James R. Kermode, 2016, J. Comput. Phys..

Special Issue: Big data and predictive computational modeling

Michele Girolami, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2016, J. Comput. Phys..

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning pdf

Liping Wang, Piyush Pandita, Nicholas Zabaras, 2021, Journal of Mechanical Design.

Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems

Souvik Chakraborty, Nicholas Zabaras, N. Zabaras, 2018, Comput. Phys. Commun..

An information-theoretic approach for obtaining property PDFs from macro specifications of microstructural variability

Veera Sundararaghavan, Nicholas Zabaras, N. Zabaras, 2005 .

Structured Bayesian Gaussian process latent variable model

N. Zabaras, Steven Atkinson, 2018, ArXiv.

Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification

Ilias Bilionis, Nicholas Zabaras, N. Zabaras, 2012, SIAM J. Sci. Comput..

Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems pdf

Nicholas Zabaras, Yingzhi Xia, N. Zabaras, 2021, J. Comput. Phys..

A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media

Peng Chen, Nicholas Zabaras, N. Zabaras, 2013, J. Comput. Phys..

Melt Flow Control in the Directional Solidification of Binary Alloys

N. Zabaras, 2003 .

Variational Reformulation of Bayesian Inverse Problems

N. Zabaras, Ilias Bilionis, Ioannis Katsounaros, 2014, 1410.5522.

Solving inverse problems using conditional invertible neural networks pdf

Nicholas Zabaras, Govinda Anantha Padmanabha, N. Zabaras, 2020, J. Comput. Phys..

Predictive Collective Variable Discovery with Deep Bayesian Models

N. Zabaras, M. Schöberl, P. Koutsourelakis, 2018, The Journal of chemical physics.

Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification

Peng Chen, Nicholas Zabaras, N. Zabaras, 2013 .

Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities

N. Zabaras, Jichun Wu, S. Mo, 2019, Water Resources Research.