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Nicholas Zabaras
Nicholas Zabaras
Professor, Cornell University
Verified email at nd.edu
Title
Cited by
Cited by
Year
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Y Zhu, N Zabaras, PS Koutsourelakis, P Perdikaris
Journal of Computational Physics 394, 56-81, 2019
9022019
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Y Zhu, N Zabaras
Journal of Computational Physics 366, 415-447, 2018
6702018
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
X Ma, N Zabaras
Journal of Computational Physics 228 (8), 3084-3113, 2009
6012009
Sparse grid collocation schemes for stochastic natural convection problems
B Ganapathysubramanian, N Zabaras
Journal of Computational Physics 225 (1), 652-685, 2007
5282007
An inverse method for determining elastic material properties and a material interface
DS Schnur, N Zabaras
International Journal for Numerical Methods in Engineering 33 (10), 2039-2057, 1992
3181992
A Bayesian inference approach to the inverse heat conduction problem
J Wang, N Zabaras
International journal of heat and mass transfer 47 (17-18), 3927-3941, 2004
3062004
Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
S Mo, Y Zhu, N Zabaras, X Shi, J Wu
Water Resources Research 55 (1), 703-728, 2019
2882019
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
N Geneva, N Zabaras
Journal of Computational Physics 403, 109056, 2020
2822020
An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations
X Ma, N Zabaras
Journal of Computational Physics 229 (10), 3884-3915, 2010
2642010
Classification and reconstruction of three-dimensional microstructures using support vector machines
V Sundararaghavan, N Zabaras
Computational Materials Science 32 (2), 223-239, 2005
2172005
Hierarchical Bayesian models for inverse problems in heat conduction
J Wang, N Zabaras
Inverse Problems 21 (1), 183, 2004
2152004
Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification
S Mo, N Zabaras, X Shi, J Wu
Water Resources Research 55 (5), 3856-3881, 2019
2112019
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification
I Bilionis, N Zabaras, BA Konomi, G Lin
Journal of Computational Physics 241, 212-239, 2013
2022013
Multi-output local Gaussian process regression: Applications to uncertainty quantification
I Bilionis, N Zabaras
Journal of Computational Physics 231 (17), 5718-5746, 2012
1862012
Using Bayesian statistics in the estimation of heat source in radiation
J Wang, N Zabaras
International Journal of Heat and Mass Transfer 48 (1), 15-29, 2005
1742005
An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method
X Ma, N Zabaras
Inverse Problems 25 (3), 035013, 2009
1712009
A level set simulation of dendritic solidification with combined features of front-tracking and fixed-domain methods
L Tan, N Zabaras
Journal of Computational Physics 211 (1), 36-63, 2006
1642006
Finite element solution of two‐dimensional inverse elastic problems using spatial smoothing
DS Schnur, N Zabaras
International Journal for Numerical Methods in Engineering 30 (1), 57-75, 1990
1571990
Finite element analysis of some inverse elasticity problems
A Maniatty, N Zabaras, K Stelson
Journal of engineering mechanics 115 (6), 1303-1317, 1989
1551989
A sensitivity analysis for the optimal design of metal-forming processes
S Badrinarayanan, N Zabaras
Computer Methods in Applied Mechanics and Engineering 129 (4), 319-348, 1996
1541996
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Articles 1–20