World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
61
Citations
11443
World Ranking
2097
National Ranking
665

I. M. Navon 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 I. M. Navon 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: 232 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

I. M. Navon 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 I. M. Navon 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: 61 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

I. M. Navon is affiliated with Florida State University in the United States and focuses on research primarily in the field of engineering. The scientist's work spans several subfields, including statistical and nonlinear physics, computational mechanics, atmospheric science, aerospace engineering, and statistics, probability, and uncertainty.

The main research topics covered by I. M. Navon include model reduction and neural networks, lattice Boltzmann simulation studies, probabilistic and robust engineering design, meteorological phenomena and simulations, nuclear engineering thermal-hydraulics, fluid dynamics and vibration analysis, and fluid dynamics and turbulent flows.

The scientist has published extensively across a variety of venues. Frequent publication venues include:

  • International Journal for Numerical Methods in Fluids
  • arXiv (Cornell University)
  • Physics of Fluids
  • Journal of Computational Physics
  • Computer Methods in Applied Mechanics and Engineering

Recent papers by I. M. Navon demonstrate a focus on machine learning methods applied to fluid flows, nonlinear dynamical systems, and geophysical flow data assimilation. Selected recent publications include:

  • Long lead-time daily and monthly streamflow forecasting using machine learning methods, 2020, Journal of Hydrology
  • Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network, 2020, Computer Methods in Applied Mechanics and Engineering
  • Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes, 2022, Computer Methods in Applied Mechanics and Engineering
  • Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows, 2020, Physics of Fluids
  • A non-linear non-intrusive reduced order model of fluid flow by auto-encoder and self-attention deep learning methods, 2023, International Journal for Numerical Methods in Engineering

Collaborative efforts are a notable aspect of the scientist's career. Frequent co-authors include Charbel Farhat, Cedric Taylor, Nigel Weatherill, Philip Gresho, and David Gartling, each having contributed to multiple joint publications with I. M. Navon.

Best Publications

  • Variational Data Assimilation with an Adiabatic Version of the NMC Spectral Model

    I. M. Navon;X. Zou;J. Derber;J. Sela

  • Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography

    I.M. Navon

  • VARIATM—A FORTRAN program for objective analysis of pseudostress wind fields using large-scale conjugate-gradient minimization

    David M. Legler;I. M. Navon

  • Conjugate-Gradient Methods for Large-Scale Minimization in Meteorology

    I. M. Navon;David M. Legler

  • Long lead-time daily and monthly streamflow forecasting using machine learning methods

    M. Cheng;F. Fang;T. Kinouchi;I.M. Navon

  • Data Assimilation for Numerical Weather Prediction: A Review

    Ionel M. Navon

  • Second-Order Information in Data Assimilation*

    Francois-Xavier Le Dimet;I. M. Navon;Dacian N. Daescu

  • A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition

    Yanhua Cao;Jiang Zhu;I. M. Navon;Zhendong Luo

  • Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods

    X. Zou;I. M. Navon;M. Berger;K. H. Phua

  • The second order adjoint analysis: Theory and applications

    Zhi Wang;I. M. Navon;F. X. Le Dimet;X. Zou

  • An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation

    X. Zou;I. M. Navon;F. X. Ledimet

  • Non-linear model reduction for the Navier-Stokes equations using residual DEIM method

    D. Xiao;F. Fang;A. G. Buchan;C. C. Pain

  • Non-intrusive reduced order modelling of the Navier-Stokes equations

    Dunhui Xiao;Dunhui Xiao;F. Fang;A.G. Buchan;C.C. Pain

  • Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems

    Dan G. Cacuci;Mihaela Ionescu-Bujor;Ionel Michael Navon

  • POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model

    R. ŞTefnescu;I. M. Navon

  • Optimality of variational data assimilation and its relationship with the Kalman filter and smoother

    Zhijin Li;I. M. Navon

  • Reduced-Order Modeling of the Upper Tropical Pacific Ocean Model using Proper Orthogonal Decomposition

    Yanhua Cao;Jiang Zhu;Zhendong Luo;I. M. Navon

  • Objective Analysis of Pseudostress over the Indian Ocean Using a Direct-Minimization Approach

    David M. Legler;I. M. Navon;James J. O'Brien

  • Mixed Finite Element Formulation and Error Estimates Based on Proper Orthogonal Decomposition for the Nonstationary Navier-Stokes Equations

    Zhendong Luo;Jing Chen;I. M. Navon;Xiaozhong Yang

  • Optimal control of cylinder wakes via suction and blowing

    Zhijin Li;I.M. Navon;M.Y. Hussaini;F.-X. Le Dimet

Frequent Co-Authors

Christopher C. Pain
Christopher C. Pain Imperial College London
Xiaolei Zou
Xiaolei Zou Nanjing University of Information Science and Technology
Jichun Li
Jichun Li University of Nevada, Las Vegas
Beny Neta
Beny Neta Naval Postgraduate School
Adrian Sandu
Adrian Sandu Virginia Tech
Peter A. Allison
Peter A. Allison Imperial College London
Zhengyu Liu
Zhengyu Liu The Ohio State University
Ana Isabel Miranda
Ana Isabel Miranda University of Aveiro
Mostafa Abbaszadeh
Mostafa Abbaszadeh Amirkabir University of Technology
Hamid Garmestani
Hamid Garmestani Georgia Institute of Technology

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