World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
56
Citations
15141
World Ranking
726
National Ranking
359

Brian R. Hunt publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Brian R. Hunt sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 148 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Brian R. Hunt D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Brian R. Hunt sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 56 D-Index — 80th percentile

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

The last bar groups every scientist with 86 D-Index or more.

Overview

Brian R. Hunt is affiliated with the University of Maryland, College Park in the United States. The primary focus of their research lies in Environmental Science, with a particular emphasis on Atmospheric Science, Artificial Intelligence, and Global and Planetary Change. Their work also spans subfields such as Ecology and Statistical and Nonlinear Physics.

The scientist's research explores several main topics, including Meteorological Phenomena and Simulations, Neural Networks and Reservoir Computing, Climate Variability and Models, Model Reduction and Neural Networks, Mental Health Research Topics, Environmental DNA in Biodiversity Studies, and Microbial Community Ecology and Physiology.

Recent publications by Brian R. Hunt include the following:

  • A Machine Learning-Based Global Atmospheric Forecast Model, 2020, Geophysical Research Letters
  • A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics-Based Numerical Model, 2022, Journal of Advances in Modeling Earth Systems
  • A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component, 2023, Geophysical Research Letters
  • Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics, 2020, Neural Networks
  • Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing, 2023, Neural Networks

The frequent co-authors in their research include Edward Ott, Evgeny A. Pakhomov, Troy Arcomano, Istvan Szunyogh, and Alexander Wikner.

Brian R. Hunt has published primarily in venues such as Technical Reports, arXiv (Cornell University), Geophysical Research Letters, Journal of Advances in Modeling Earth Systems, and Neural Networks.

Best Publications

  • Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter

    Brian R. Hunt;Eric J. Kostelich;Istvan Szunyogh

  • Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

    Jaideep Pathak;Brian Hunt;Michelle Girvan;Zhixin Lu

  • A local ensemble Kalman filter for atmospheric data assimilation

    Edward Ott;Brian R. Hunt;Istvan Szunyogh;Aleksey V. Zimin

  • Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

    Jaideep Pathak;Zhixin Lu;Brian R. Hunt;Michelle Girvan

  • Prevalence: a translation-invariant “almost every” on infinite-dimensional spaces

    Brian R. Hunt;Timothy Sauer;James A. Yorke

  • Reducing storage requirements for biological sequence comparison

    Michael Roberts;Wayne Hayes;Brian R. Hunt;Stephen M. Mount

  • A Guide to Matlab: For Beginners and Experienced Users

    Brian R. Hunt;Ronald L. Lipsman;Jonathan M. Rosenberg

  • Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics

    Pantelis R. Vlachas;Jaideep Pathak;Brian R. Hunt;Themistoklis P. Sapsis

  • Onset of synchronization in large networks of coupled oscillators.

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Reservoir observers: Model-free inference of unmeasured variables in chaotic systems

    Zhixin Lu;Jaideep Pathak;Brian Hunt;Michelle Girvan

  • Four-dimensional ensemble Kalman filtering

    B. R. Hunt;E. Kalnay;Eric Kostelich;E. Ott

  • Characterizing the Dynamical Importance of Network Nodes and Links

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Balance and Ensemble Kalman Filter Localization Techniques

    Steven J. Greybush;Eugenia Kalnay;Takemasa Miyoshi;Kayo Ide

  • Differentiable generalized synchronization of chaos

    Brian R. Hunt;Edward Ott;James A. Yorke

  • Local low dimensionality of atmospheric dynamics.

    D. J. Patil;Brian R. Hunt;Eugenia Kalnay;James A. Yorke

  • Map with more than 100 coexisting low-period periodic attractors.

    Ulrike Feudel;Celso Grebogi;Brian R. Hunt;James A. Yorke

  • Approximating the largest eigenvalue of network adjacency matrices.

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

    Istvan Szunyogh;Eric J. Kostelich;G. Gyarmati;D. J. Patil

  • A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96

    Elana J. Fertig;John Harlim;Brian R. Hunt

  • The Hausdorff dimension of graphs of Weierstrass functions

    Brian Hunt

  • The theory of chaotic attractors

    B.R. Hunt;J.A. Kennedy;T.-Y. Li;Helena Nusse

Frequent Co-Authors

Edward Ott
Edward Ott University of Maryland, College Park
James A. Yorke
James A. Yorke University of Maryland, College Park
Eugenia Kalnay
Eugenia Kalnay University of Maryland, College Park
Jonathan Rosenberg
Jonathan Rosenberg University of Maryland, College Park
Kevin R. Coombes
Kevin R. Coombes The Ohio State University
Celso Grebogi
Celso Grebogi University of Aberdeen
Timothy Sauer
Timothy Sauer George Mason University
Petros Koumoutsakos
Petros Koumoutsakos Harvard University
Daniel P. Lathrop
Daniel P. Lathrop University of Maryland, College Park
James A. Carton
James A. Carton University of Maryland, College Park

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