World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
85
Citations
37645
World Ranking
379
National Ranking
126

J. Nathan Kutz 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 J. Nathan Kutz 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: 532 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

J. Nathan Kutz 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 J. Nathan Kutz 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: 85 D-Index — 96th percentile

96% of scientists in this discipline score the same or lower.

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

Overview

J. Nathan Kutz is affiliated with the University of Washington in the United States. Their research spans multiple fields, primarily focused on Engineering and Physics and Astronomy. They contribute extensively to the subfields of Statistical and Nonlinear Physics, Artificial Intelligence, Control and Systems Engineering, Computational Mechanics, and Statistics, Probability and Uncertainty.

Their work encompasses a range of interconnected topics including Model Reduction and Neural Networks, Probabilistic and Robust Engineering Design, Fluid Dynamics and Turbulent Flows, Control Systems and Identification, Fault Detection and Control Systems, Cardiac Arrest and Resuscitation, as well as Fluid Dynamics and Vibration Analysis.

J. Nathan Kutz has published prolifically, with frequent contributions appearing in these venues:

  • arXiv (Cornell University)
  • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • IEEE Access
  • Zenodo (CERN European Organization for Nuclear Research)
  • Circulation

Recent papers include:

  • Modern Koopman Theory for Dynamical Systems, 2022, SIAM Review
  • Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control, 2022, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics, 2020, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • Shallow neural networks for fluid flow reconstruction with limited sensors, 2020, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data, 2020, The Journal of Open Source Software

The scientist has collaborated frequently with a core group of coauthors, including Steven L. Brunton, Bingni W. Brunton, Heemun Kwok, Jason Coult, and Thomas D. Rea.

J. Nathan Kutz is also an author of books published by Springer Nature, including a recent work titled Model Order Reduction and Applications (2023).

Best Publications

  • Discovering governing equations from data by sparse identification of nonlinear dynamical systems

    Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • On dynamic mode decomposition: Theory and applications

    Jonathan H. Tu;Clarence Worth Rowley;Dirk M. Luchtenburg;Steven L. Brunton

  • Data-driven discovery of partial differential equations.

    Samuel H. Rudy;Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Deep learning for universal linear embeddings of nonlinear dynamics.

    Bethany Lusch;J. Nathan Kutz;Steven L. Brunton

  • Dynamic Mode Decomposition with Control

    Joshua L. Proctor;Steven L. Brunton;J. Nathan Kutz

  • Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

    J. Nathan Kutz;Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor

  • Deep learning in fluid dynamics

    J. Nathan Kutz

  • Data-driven discovery of coordinates and governing equations

    Kathleen Champion;Bethany Lusch;J. Nathan Kutz;Steven L. Brunton

  • Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

    Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

    Steven L. Brunton;J. Nathan Kutz

  • Chaos as an intermittently forced linear system.

    Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor;Eurika Kaiser

  • Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

    Bingni W. Brunton;Lise A. Johnson;Jeffrey G. Ojemann;J. Nathan Kutz

  • Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.

    E. Kaiser;J. N. Kutz;S. L. Brunton

  • Data-Driven Sparse Sensor Placement for Reconstruction

    Krithika Manohar;Bingni W. Brunton;J. Nathan Kutz;Steven L. Brunton

  • Multiresolution Dynamic Mode Decomposition

    J. Nathan Kutz;Xing Fu;Steven L. Brunton

  • Modern Koopman Theory for Dynamical Systems.

    Steven L. Brunton;Marko Budisic;Eurika Kaiser;J. Nathan Kutz

  • Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

    Eurika Kaiser;J. Nathan Kutz;Steven Brunton

  • Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

    Krithika Manohar;Bingni W. Brunton;J. Nathan Kutz;Steven L. Brunton

  • Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics

    Niall M. Mangan;Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

    J. Nathan Kutz

  • Bose-Einstein condensates in standing waves: the cubic nonlinear Schrödinger equation with a periodic potential.

    Jared C. Bronski;Lincoln D. Carr;Bernard Deconinck;J. Nathan Kutz

  • Neural Networks and Deep Learning

    Steven L. Brunton;J. Nathan Kutz

Frequent Co-Authors

Steven T. Cundiff
Steven T. Cundiff University of Michigan–Ann Arbor
Ping-kong Alexander Wai
Ping-kong Alexander Wai Hong Kong Polytechnic University
Herbert G. Winful
Herbert G. Winful University of Michigan–Ann Arbor
Frank W. Wise
Frank W. Wise Cornell University
Roberto Morandotti
Roberto Morandotti Institut National de la Recherche Scientifique
Demetrios N. Christodoulides
Demetrios N. Christodoulides University of Southern California
Richard P. Mirin
Richard P. Mirin National Institute of Standards and Technology
Clarence W. Rowley
Clarence W. Rowley Princeton University
Keren Bergman
Keren Bergman Columbia University
William L. Kath
William L. Kath Northwestern University

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