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 | 903 | 2019 |

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification Y Zhu, N Zabaras Journal of Computational Physics 366, 415-447, 2018 | 670 | 2018 |

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 | 602 | 2009 |

Sparse grid collocation schemes for stochastic natural convection problems B Ganapathysubramanian, N Zabaras Journal of Computational Physics 225 (1), 652-685, 2007 | 528 | 2007 |

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 | 318 | 1992 |

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 | 306 | 2004 |

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 | 289 | 2019 |

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks N Geneva, N Zabaras Journal of Computational Physics 403, 109056, 2020 | 282 | 2020 |

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 | 264 | 2010 |

Classification and reconstruction of three-dimensional microstructures using support vector machines V Sundararaghavan, N Zabaras Computational Materials Science 32 (2), 223-239, 2005 | 217 | 2005 |

Hierarchical Bayesian models for inverse problems in heat conduction J Wang, N Zabaras Inverse Problems 21 (1), 183, 2004 | 215 | 2004 |

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 | 212 | 2019 |

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 | 202 | 2013 |

Multi-output local Gaussian process regression: Applications to uncertainty quantification I Bilionis, N Zabaras Journal of Computational Physics 231 (17), 5718-5746, 2012 | 186 | 2012 |

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 | 174 | 2005 |

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 | 171 | 2009 |

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 | 164 | 2006 |

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 | 157 | 1990 |

Finite element analysis of some inverse elasticity problems A Maniatty, N Zabaras, K Stelson Journal of engineering mechanics 115 (6), 1303-1317, 1989 | 155 | 1989 |

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 | 154 | 1996 |