World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
43
Citations
8632
World Ranking
6069
National Ranking
1693

Alexandre M. Tartakovsky 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 Alexandre M. Tartakovsky 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: 277 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Alexandre M. Tartakovsky 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 Alexandre M. Tartakovsky 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: 43 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

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

Overview

Alexandre M. Tartakovsky is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily lies within the field of Engineering, with notable contributions in subfields such as Statistical and Nonlinear Physics, Computational Mechanics, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty, and Artificial Intelligence.

The research topics covered in Tartakovsky's work include:

  • Model Reduction and Neural Networks
  • Probabilistic and Robust Engineering Design
  • Gaussian Processes and Bayesian Inference
  • Reservoir Engineering and Simulation Methods
  • Groundwater flow and contamination studies
  • Meteorological Phenomena and Simulations
  • Advanced battery technologies research

Significant recent publications by Tartakovsky include:

  • Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems, 2020, Water Resources Research
  • Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport, 2020, Advances in Water Resources
  • Differentiable modelling to unify machine learning and physical models for geosciences, 2023, Nature Reviews Earth & Environment
  • Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations, 2021, Water Resources Research
  • Learning unknown physics of non-Newtonian fluids, 2021, Physical Review Fluids

Frequent co-authors associated with Tartakovsky's work include:

  • David A. Barajas-Solano
  • Qizhi He
  • Yifei Zong
  • Ramakrishna Tipireddy
  • Panos Stinis

Their research outputs have appeared in various publication venues, with recurring contributions in:

  • arXiv (Cornell University)
  • Journal of Computational Physics
  • Computer Methods in Applied Mechanics and Engineering
  • Water Resources Research
  • SSRN Electronic Journal

Best Publications

  • Modeling and simulation of pore-scale multiphase fluid flow and reactive transport in fractured and porous media

    Paul Meakin;Paul Meakin;Alexandre M. Tartakovsky

  • Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

    A. M. Tartakovsky;C. Ortiz Marrero;Paris Perdikaris;G. D. Tartakovsky

  • Modeling of surface tension and contact angles with smoothed particle hydrodynamics

    Alexandre M. Tartakovsky;Paul Meakin

  • Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport

    Qi Zhi He;David Barajas-Solano;Guzel Tartakovsky;Alexandre M. Tartakovsky

  • Differentiable modelling to unify machine learning and physical models for geosciences

    Unknown

  • Simulations of reactive transport and precipitation with smoothed particle hydrodynamics

    Alexandre M. Tartakovsky;Paul Meakin;Timothy D. Scheibe;Rogene M. Eichler West

  • Mixing-induced precipitation: Experimental study and multiscale numerical analysis

    Alexandre M. Tartakovsky;George D. Redden;Peter C. Lichtner;Timothy D. Scheibe

  • Flow intermittency, dispersion, and correlated continuous time random walks in porous media

    Pietro de Anna;Pietro de Anna;Tanguy Le Borgne;Marco Dentz;Alexandre M. Tartakovsky

  • Pore scale modeling of immiscible and miscible fluid flows using smoothed particle hydrodynamics

    Alexandre M. Tartakovsky;Paul Meakin

  • A smoothed particle hydrodynamics model for miscible flow in three-dimensional fractures and the two-dimensional Rayleigh-Taylor instability

    Alexandre M. Tartakovsky;Paul Meakin

  • A smoothed particle hydrodynamics model for reactive transport and mineral precipitation in porous and fractured porous media

    Alexandre M. Tartakovsky;Paul Meakin;Timothy D. Scheibe;Brian D. Wood

  • Intercomparison of 3D pore-scale flow and solute transport simulation methods

    Xiaofan Yang;Yashar Mehmani;William A. Perkins;Andrea Pasquali

  • On breakdown of macroscopic models of mixing-controlled heterogeneous reactions in porous media

    I. Battiato;D.M. Tartakovsky;A.M. Tartakovsky;T. Scheibe

  • Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow

    Alexandre M. Tartakovsky;Alexander Panchenko

  • Hybrid models of reactive transport in porous and fractured media

    Ilenia Battiato;Daniel M. Tartakovsky;Alexandre M. Tartakovsky;Timothy D. Scheibe

  • A new smoothed particle hydrodynamics non-Newtonian model for friction stir welding: Process modeling and simulation of microstructure evolution in a magnesium alloy

    Wenxiao Pan;Dongsheng Li;Alexandre M. Tartakovsky;Said Ahzi;Said Ahzi

  • An efficient, high-order probabilistic collocation method on sparse grids for three-dimensional flow and solute transport in randomly heterogeneous porous media

    Guang Lin;Alexandre M. Tartakovsky

  • EFFECTS OF INCOMPLETE MIXING ON MULTICOMPONENT REACTIVE TRANSPORT

    Alexandre M. Tartakovsky;Guzel D. Tartakovsky;Timothy D. Scheibe

  • Smoothed particle hydrodynamics and its applications for multiphase flow and reactive transport in porous media

    Alexandre M. Tartakovsky;Nathaniel Trask;K. Pan;Bruce D. Jones

  • Pore-scale study of capillary trapping mechanism during CO2 injection in geological formations

    Uditha C. Bandara;Alexandre M. Tartakovsky;Bruce J. Palmer

  • Hybrid Simulations of Reaction-Diffusion Systems in Porous Media

    A. M. Tartakovsky;D. M. Tartakovsky;T. D. Scheibe;P. Meakin

  • Stochastic langevin model for flow and transport in porous media.

    Alexandre M. Tartakovsky;Daniel M. Tartakovsky;Paul Meakin

Frequent Co-Authors

Marco Dentz
Marco Dentz Spanish National Research Council
Diogo Bolster
Diogo Bolster University of Notre Dame
Cristina H. Amon
Cristina H. Amon University of Toronto
Shlomo P. Neuman
Shlomo P. Neuman University of Arizona
Paris Perdikaris
Paris Perdikaris University of Pennsylvania
J. Nathan Kutz
J. Nathan Kutz University of Washington
George Em Karniadakis
George Em Karniadakis Brown University
Guang Lin
Guang Lin Purdue University West Lafayette
Jie Bao
Jie Bao University of New South Wales
Mart Oostrom
Mart Oostrom Pacific Northwest National Laboratory

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