World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
42
Citations
12007
World Ranking
1751
National Ranking
748

Engineering and Technology

D-Index
42
Citations
12018
World Ranking
6376
National Ranking
1748

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

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