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Engineering and Technology
USA
2026
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Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
91
Citations
32201
World Ranking
70
National Ranking
43

Engineering and Technology

D-Index
98
Citations
36568
World Ranking
161
National Ranking
58

Weinan E 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 Weinan E 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: 267 publications — 80th percentile

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

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

Weinan E 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 Weinan E 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: 91 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in United States Leader Award
  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Engineering and Technology in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2020 - ACM Gordon Bell Prize For "Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning"
  • 2013 - Fellow of the American Mathematical Society
  • 2009 - SIAM Fellow For analysis of multiscale and stochastic problems.

Overview

Weinan E is affiliated with Princeton University in the United States. Their primary research contributions fall within Computer Science, particularly focusing on subfields such as Materials Chemistry, Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, and Computational Mechanics.

The scientist's work addresses key topics including Machine Learning in Materials Science, Model Reduction and Neural Networks, Neural Networks and Applications, Protein Structure and Dynamics, X-ray Diffraction in Crystallography, Stochastic Gradient Optimization Techniques, and Computational Drug Discovery Methods.

Frequent collaborators of Weinan E include Linfeng Zhang, Han Wang, Guolin Ke, Roberto Car, and Jiequn Han.

They have published extensively, with a significant number of papers in venues such as arXiv, Computer Physics Communications, SSRN Electronic Journal, Zenodo, and Physical Review B.

Notable recent papers include:

  • DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, 2020, Computer Physics Communications
  • DeePMD-kit v2: A software package for deep potential models, 2023, The Journal of Chemical Physics
  • Phase Diagram of a Deep Potential Water Model, 2021, Physical Review Letters
  • 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy, 2020, Computer Physics Communications
  • A deep potential model with long-range electrostatic interactions, 2022, The Journal of Chemical Physics

Weinan E has contributed to academic literature in book form as well, with a publication titled Introduction to Data Science released in 2022 by WSPC/HEP eBooks.

Their work has been recognized through several awards, including the ACM Gordon Bell Prize in 2020 for pushing the limits of molecular dynamics simulations with ab initio accuracy. They were also named a Fellow of the American Mathematical Society in 2013 and a SIAM Fellow in 2009 for contributions to the analysis of multiscale and stochastic problems.

Best Publications

  • Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

    Linfeng Zhang;Jiequn Han;Han Wang;Roberto Car

  • DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

    Han Wang;Linfeng Zhang;Jiequn Han;Weinan E

  • Solving high-dimensional partial differential equations using deep learning

    Jiequn Han;Arnulf Jentzen;Weinan E

  • The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems

    Weinan E;Weinan E;Bing Yu

  • String method for the study of rare events

    Weinan E;Weiqing Ren;Eric Vanden-Eijnden

  • Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations

    Weinan E;Weinan E;Jiequn Han;Arnulf Jentzen

  • The Heterognous Multiscale Methods

    Weinan E;Bjorn Engquist

  • DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

    Yuzhi Zhang;Haidi Wang;Weijie Chen;Jinzhe Zeng

  • Heterogeneous multiscale methods: A review

    Weinan E;Bjorn Engquist;Xiantao Li;Weiqing Ren

  • Simplified and improved string method for computing the minimum energy paths in barrier-crossing events.

    Weinan E;Weiqing Ren;Eric Vanden-Eijnden

  • Onsager's conjecture on the energy conservation for solutions of Euler's equation

    Peter Constantin;Weinan E;Edriss S. Titi

  • A Proposal on Machine Learning via Dynamical Systems

    Weinan E;Weinan E

  • Generalized variational principles, global weak solutions and behavior with random initial data for systems of conservation laws arising in adhesion particle dynamics

    Weinan E;Yu G. Rykov;Yakov G. Sinai

  • Transition-Path Theory and Path-Finding Algorithms for the Study of Rare Events

    Weinan E;Eric Vanden-Eijnden

  • The heterogeneous multiscale method

    Assyr Abdulle;Weinan E;Weinan E;Björn Engquist;Eric Vanden-Eijnden

  • Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation.

    Linfeng Zhang;De-Ye Lin;Han Wang;Roberto Car

  • Active learning of uniformly accurate interatomic potentials for materials simulation

    Linfeng Zhang;De Ye Lin;Han Wang;Roberto Car

  • Towards a Theory of Transition Paths

    E Weinan;Eric Vanden-Eijnden

  • Finite temperature string method for the study of rare events.

    Weinan E;Weiqing Ren;Eric Vanden-Eijnden

  • The Heterogeneous Multiscale Method: A Review

    Weinan E;Bjorn Engquist;Xiantao Li;Weiqing Ren

  • Invariant measures for Burgers equation with stochastic forcing

    Weinan E;Konstantin Khanin;Alexander Mazel;Yakov Sinai

Frequent Co-Authors

Eric Vanden-Eijnden
Eric Vanden-Eijnden Courant Institute of Mathematical Sciences
Jianfeng Lu
Jianfeng Lu Duke University
Björn Engquist
Björn Engquist The University of Texas at Austin
Roberto Car
Roberto Car Princeton University
Arnulf Jentzen
Arnulf Jentzen Chinese University of Hong Kong, Shenzhen
Lin Lin
Lin Lin University of California, Berkeley
Chi-Wang Shu
Chi-Wang Shu Brown University
Lexing Ying
Lexing Ying Stanford University
Mark E. Tuckerman
Mark E. Tuckerman New York University
Yakov G. Sinai
Yakov G. Sinai Princeton University

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