World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
62
Citations
12764
World Ranking
1929
National Ranking
622

Nicholas Zabaras publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Nicholas Zabaras sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 193 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Nicholas Zabaras D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Nicholas Zabaras sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 62 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Nicholas Zabaras is affiliated with the University of Notre Dame in the United States and focuses on research in the field of Computer Science. Their work spans multiple subfields, including Statistical and Nonlinear Physics, Artificial Intelligence, Computational Theory and Mathematics, Environmental Engineering, and Materials Chemistry.

Their research addresses several main topics, such as Model Reduction and Neural Networks, Advanced Multi-Objective Optimization Algorithms, Groundwater Flow and Contamination Studies, Gaussian Processes and Bayesian Inference, Machine Learning in Materials Science, Protein Structure and Dynamics, and Water Systems and Optimization.

Zabaras has contributed publications to various academic venues. Frequent publication venues include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Water Resources Research
  • Journal of Computational Physics
  • Neural Networks

Recent papers by Nicholas Zabaras cover topics in hydraulic conductivities, physical system modeling, inverse problem-solving, and deep learning approaches applied to environmental and computational physics problems. Notable recent publications include:

  • "Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities" (2020, Water Resources Research)
  • "Transformers for modeling physical systems" (2021, Neural Networks)
  • "Solving inverse problems using conditional invertible neural networks" (2021, Journal of Computational Physics)
  • "Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties" (2022, Water Resources Research)
  • "Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems" (2022, Journal of Computational Physics)

Zabaras frequently collaborates with several researchers across their publications. Common co-authors include:

  • Nicholas Geneva
  • Govinda Anantha Padmanabha
  • Cheng Peng
  • Valeria Andreoli
  • Steven Atkinson

Best Publications

  • Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

    Yinhao Zhu;Nicholas Zabaras;Phaedon-Stelios Koutsourelakis;Paris Perdikaris

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

    Yinhao Zhu;Nicholas Zabaras

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

    Xiang Ma;Nicholas Zabaras

  • Sparse grid collocation schemes for stochastic natural convection problems

    Baskar Ganapathysubramanian;Nicholas Zabaras

  • A Bayesian inference approach to the inverse heat conduction problem

    Jingbo Wang;Nicholas Zabaras

  • Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media

    Shaoxing Mo;Shaoxing Mo;Yinhao Zhu;Nicholas Zabaras;Xiaoqing Shi

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

    D. S. Schnur;Nicholas Zabaras

  • Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

    Nicholas Geneva;Nicholas Zabaras

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

    Xiang Ma;Nicholas Zabaras

  • Hierarchical Bayesian models for inverse problems in heat conduction

    Jingbo Wang;Nicholas Zabaras

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

    Veeraraghavan Sundararaghavan;Nicholas Zabaras

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

    Ilias Bilionis;Nicholas Zabaras;Bledar A. Konomi;Guang Lin

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

    Ilias Bilionis;Nicholas Zabaras

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

    Xiang Ma;Nicholas Zabaras

  • Using Bayesian statistics in the estimation of heat source in radiation

    Jingbo Wang;Nicholas Zabaras

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

    Lijian Tan;Nicholas Zabaras

  • Transformers for Modeling Physical Systems

    Nicholas Geneva;Nicholas Zabaras

  • Finite Element Analysis of Some Inverse Elasticity Problems

    Antoinette Maniatty;Nicholas Zabaras;Kim Stelson

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

    D. S. Schnur;Nicholas Zabaras

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

    S. Badrinarayanan;Nicholas Zabaras

  • Deep Autoregressive Neural Networks for High-Dimensional Inverse Problems in Groundwater Contaminant Source Identification

    Shaoxing Mo;Shaoxing Mo;Nicholas Zabaras;Xiaoqing Shi;Jichun Wu

Frequent Co-Authors

Baskar Ganapathysubramanian
Baskar Ganapathysubramanian Iowa State University
Subrata Mukherjee
Subrata Mukherjee Cornell University
Jichun Wu
Jichun Wu Nanjing University
Abul Fazal M. Arif
Abul Fazal M. Arif McMaster University
Guang Lin
Guang Lin Purdue University West Lafayette
Mark Girolami
Mark Girolami University of Cambridge
Paris Perdikaris
Paris Perdikaris University of Pennsylvania
Dongbin Xiu
Dongbin Xiu The Ohio State University
Shenyang Y. Hu
Shenyang Y. Hu Pacific Northwest National Laboratory
Fei Gao
Fei Gao University of Michigan–Ann Arbor

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