World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
42
Citations
12007
World Ranking
1751
National Ranking
749

Engineering and Technology

D-Index
42
Citations
12018
World Ranking
6378
National Ranking
1749

Howard C. Elman 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 Howard C. Elman 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: 83 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: 141 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Howard C. Elman 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 Howard C. Elman 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: 138 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: 42 D-Index — 51st percentile

51% 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

  • 2009 - SIAM Fellow For contributions to numerical linear algebra and applications to finite elements and computational fluid dynamics.

Overview

Howard C. Elman is affiliated with the University of Maryland, College Park in the United States. Their research spans multiple fields of study, primarily focusing on Physics and Astronomy as well as Engineering. Within these areas, their work explores a range of subfields including Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics, Aerospace Engineering, Artificial Intelligence, and Control and Systems Engineering.

The scientist has contributed to topics such as Probabilistic and Robust Engineering Design, Model Reduction and Neural Networks, Nuclear Reactor Physics and Engineering, Gaussian Processes and Bayesian Inference, Matrix Theory and Algorithms, Magnetic Confinement Fusion Research, and Fusion Materials and Technologies.

Howard C. Elman has authored research papers published in various venues with varying frequency. The most frequent publication venues include:

  • arXiv (Cornell University)
  • Machine Learning Science and Technology
  • Computer Methods in Applied Mechanics and Engineering
  • Applications of Mathematics
  • BIT Numerical Mathematics

Notable recent papers authored or coauthored by Howard C. Elman include:

  • "A low-rank solver for the stochastic unsteady Navier-Stokes problem," 2020, Computer Methods in Applied Mechanics and Engineering
  • "Surrogate Approximation of the Grad-Shafranov Free Boundary Problem via Stochastic Collocation on Sparse Grids," 2021, arXiv (Cornell University)

Other relevant recent papers coauthored by colleagues in related work include:

  • "Novel Deep neural networks for solving Bayesian statistical inverse," 2021, arXiv (Cornell University)
  • "A deep neural network approach for parameterized PDEs and Bayesian inverse problems," 2023, Machine Learning Science and Technology
  • "On surrogate learning for linear stability assessment of Navier-Stokes equations with stochastic viscosity," 2022, Applications of Mathematics

Frequent coauthors of Howard C. Elman comprise:

  • Jiaxing Liang
  • Tonatiuh Sánchez-Vizuet
  • Kookjin Lee
  • Harbir Antil
  • Akwum Onwunta

In recognition of contributions to numerical linear algebra and applications in finite elements and computational fluid dynamics, Howard C. Elman was named a SIAM Fellow in 2009.

Best Publications

  • Finite Elements and Fast Iterative Solvers: with Applications in Incompressible Fluid Dynamics

    Howard C. Elman;David J. Silvester;Andrew J. Wathen

  • Variational Iterative Methods for Nonsymmetric Systems of Linear Equations

    Stanley C. Eisenstat;Howard C. Elman;Martin H. Schultz

  • Finite Elements and Fast Iterative Solvers: with Applications in Incompressible Fluid Dynamics

    Unknown

  • Inexact and preconditioned Uzawa algorithms for saddle point problems

    Howard C. Elman;Gene H. Golub

  • Iterative methods for large, sparse, nonsymmetric systems of linear equations

    Howard C. Elman

  • Algorithm 866: IFISS, a Matlab toolbox for modelling incompressible flow

    Howard C. Elman;Alison Ramage;David J. Silvester

  • Fast nonsymmetric iterations and preconditioning for Navier-Stokes equations

    Howard Elman;David Silvester

  • A Multigrid Method Enhanced by Krylov Subspace Iteration for Discrete Helmholtz Equations

    Howard C. Elman;Oliver G. Ernst;Dianne P. O'Leary

  • Preconditioning for the Steady-State Navier--Stokes Equations with Low Viscosity

    Howard C. Elman

  • Efficient preconditioning of the linearized Navier—Stokes equations for incompressible flow

    David Silvester;Howard Elman;David Kay;Andrew Wathen

  • Performance and analysis of saddle point preconditioners for the discrete steady-state Navier-Stokes equations

    Howard C. Elman;David J. Silvester;Andrew J. Wathen

  • Block Preconditioners Based on Approximate Commutators

    Howard Elman;Victoria E. Howle;John Shadid;Robert Shuttleworth

  • A taxonomy and comparison of parallel block multi-level preconditioners for the incompressible Navier-Stokes equations

    Howard Elman;V.E. Howle;John Shadid;Robert Shuttleworth

  • Block-diagonal preconditioning for spectral stochastic finite-element systems

    Catherine E. Powell;Howard C. Elman

  • A stability analysis of incomplete LU factorizations

    Howard C Elman

  • Multigrid and Krylov subspace methods for the discrete Stokes equations

    Howard C. Elman

  • IFISS: a computational laboratory for investigating incompressible flow problems

    Howard C. Elman;Alison Ramage;David J. Silvester

  • Preconditioners for saddle point problems arising in computational fluid dynamics

    Howard C. Elman

  • DESIGN UNDER UNCERTAINTY EMPLOYING STOCHASTIC EXPANSION METHODS

    Michael S. Eldred;Howard C. Elman

  • A hybrid Chebyshev Krylov subspace algorithm for solving nonsymmetric systems of linear equations

    Howard C Elman;Youcef Saad;Paul E Saylor

  • Preconditioning by fast direct methods for nonself-adjoint nonseparable elliptic equations

    Howard C Elman;Martin H Schultz

Frequent Co-Authors

Andrew J. Wathen
Andrew J. Wathen University of Oxford
Raymond S. Tuminaro
Raymond S. Tuminaro Sandia National Laboratories
Dianne P. O'Leary
Dianne P. O'Leary University of Maryland, College Park
John N. Shadid
John N. Shadid Sandia National Laboratories
Gene H. Golub
Gene H. Golub Stanford University
Ivo Babuška
Ivo Babuška The University of Texas at Austin
Yousef Saad
Yousef Saad University of Minnesota
Michele Benzi
Michele Benzi Scuola Normale Superiore di Pisa
Franco Brezzi
Franco Brezzi National Research Council (CNR)
Andrew G. Salinger
Andrew G. Salinger Sandia National Laboratories

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, expanding skill sets through related online degrees can open diverse career opportunities. Many aspiring professionals consider an can you transfer mba programs option to maximize flexibility and apply existing credits toward a master’s degree in business administration. This pathway is especially useful for those aiming to combine mathematical expertise with leadership skills.

Another popular choice is enrolling in a masters data analytics program. It leverages mathematical foundations to analyze complex data, preparing students for careers in finance, technology, and research. Data analytics is a growing field that perfectly complements a mathematics degree.

For students who prioritize ease of admission, exploring the easiest mba program options could provide accessible pathways to advancing careers. These programs offer convenient entry criteria while maintaining quality education.

Online education platforms have also expanded with flexible formats, including some of the easiest online mba degree programs. Such programs allow working professionals or international students to enhance their credentials without disrupting their current careers.

Best Scientists Citing Howard C. Elman

Trending Scientists

Recently Published Articles