H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics H-index 31 Citations 6,221 139 World Ranking 1907 National Ranking 14
Engineering and Technology H-index 38 Citations 9,163 236 World Ranking 2840 National Ranking 18

Research.com Recognitions

Awards & Achievements

2018 - Fellow of the American Association for the Advancement of Science (AAAS)

2013 - Fellow of the American Mathematical Society

2011 - SIAM Fellow For contributions to implicit methods for the solution of partial differential equations and dedicated service to the scientific community.

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Statistics
  • Programming language

David E. Keyes focuses on Domain decomposition methods, Iterative method, Applied mathematics, Mathematical optimization and Partial differential equation. His Domain decomposition methods research incorporates elements of Numerical partial differential equations and Theoretical computer science. His Iterative method research is multidisciplinary, incorporating perspectives in Jacobian matrix and determinant, Mathematical analysis and Rate of convergence.

His study looks at the relationship between Applied mathematics and topics such as Preconditioner, which overlap with Discretization, Computational fluid dynamics and Grid. The Mathematical optimization study combines topics in areas such as Kalman filter and Bayesian probability, Frequentist inference, Bayesian inference. His Partial differential equation study integrates concerns from other disciplines, such as Algorithm, Parallel computing and Nonlinear system.

His most cited work include:

  • Jacobian-free Newton-Krylov methods: a survey of approaches and applications (1313 citations)
  • The International Exascale Software Project roadmap (580 citations)
  • Convergence Analysis of Pseudo-Transient Continuation (202 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Parallel computing, Applied mathematics, Computational science, Algorithm and Domain decomposition methods. David E. Keyes interconnects Matrix, Scalability and Solver in the investigation of issues within Parallel computing. His Applied mathematics research incorporates elements of Boundary value problem, Iterative method, Mathematical optimization, Nonlinear system and Discretization.

David E. Keyes is interested in Newton's method, which is a field of Nonlinear system. David E. Keyes combines topics linked to Rate of convergence with his work on Algorithm. The study incorporates disciplines such as Partial differential equation, Mathematical analysis and Preconditioner in addition to Domain decomposition methods.

He most often published in these fields:

  • Parallel computing (26.94%)
  • Applied mathematics (19.17%)
  • Computational science (16.84%)

What were the highlights of his more recent work (between 2016-2021)?

  • Parallel computing (26.94%)
  • Algorithm (17.36%)
  • Matrix (9.33%)

In recent papers he was focusing on the following fields of study:

His main research concerns Parallel computing, Algorithm, Matrix, Supercomputer and Computational science. His Parallel computing research includes themes of Scalability and Singular value decomposition. His Matrix study combines topics from a wide range of disciplines, such as Partial differential equation and Linear algebra.

When carried out as part of a general Supercomputer research project, his work on Petascale computing is frequently linked to work in Research center, Context and General-purpose computing on graphics processing units, therefore connecting diverse disciplines of study. His Computational science research integrates issues from Computation, Boundary value problem, Boundary, Solver and Kernel. His Applied mathematics research focuses on Newton's method and how it connects with Domain decomposition methods.

Between 2016 and 2021, his most popular works were:

  • Big data and extreme-scale computing: Pathways to Convergence-Toward a shaping strategy for a future software and data ecosystem for scientific inquiry (53 citations)
  • Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities (27 citations)
  • Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures (24 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Statistics
  • Algorithm

His primary scientific interests are in Parallel computing, Matrix, Linear algebra, Covariance matrix and Memory footprint. David E. Keyes studied Parallel computing and Synchronization that intersect with Concurrency, Petascale computing, Overhead and Tensor. His Matrix study incorporates themes from Kernel and Computational science.

The various areas that David E. Keyes examines in his Memory footprint study include Linear system and Cholesky decomposition. His work carried out in the field of Statistical model brings together such families of science as Software and Solver. As a part of the same scientific family, he mostly works in the field of Multigrid method, focusing on Truncation error and, on occasion, Mathematical optimization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Jacobian-free Newton-Krylov methods: a survey of approaches and applications

D. A. Knoll;D. E. Keyes.
Journal of Computational Physics (2004)

1935 Citations

The International Exascale Software Project roadmap

Jack Dongarra;Pete Beckman;Terry Moore;Patrick Aerts.
ieee international conference on high performance computing data and analytics (2011)

802 Citations

A comparison of domain decomposition techniques for elliptic partial differential equations and their parallel implementation

David E. Keyes;William D. Gropp.
Siam Journal on Scientific and Statistical Computing (1987)

302 Citations

Multiphysics simulations: Challenges and opportunities

David E Keyes;Lois C Mcinnes;Carol Woodward;William Gropp.
ieee international conference on high performance computing data and analytics (2013)

294 Citations

Numerical Solution of Two-Dimensional Axisymmetric Laminar Diffusion Flames

M. D. Smooke;R. E. Mitchell;D. E. Keyes.
Combustion Science and Technology (1986)

291 Citations

Convergence Analysis of Pseudo-Transient Continuation

C. T. Kelley;David E. Keyes.
SIAM Journal on Numerical Analysis (1998)

287 Citations

Nonlinearly Preconditioned Inexact Newton Algorithms

Xiao-Chuan Cai;David E. Keyes.
SIAM Journal on Scientific Computing (2002)

251 Citations

High-performacne parallel implicit CFD

William D. Gropp;Dinesh K. Kaushik;David E. Keyes;Barry F. Smith.
parallel computing (2001)

200 Citations

Parallel Newton--Krylov--Schwarz Algorithms for the Transonic Full Potential Equation

Xiao-Chuan Cai;William D. Gropp;David E. Keyes;David E. Keyes;Robin G. Melvin.
SIAM Journal on Scientific Computing (1998)

169 Citations

Large-Scale Inverse Problems and Quantification of Uncertainty

Lorenz Biegler;George Biros;Omar Nabih Ghattas;Matthias Heinkenschloss.
(2010)

161 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Top Scientists Citing David E. Keyes

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Enrique S. Quintana-Ortí

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John N. Shadid

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Oak Ridge National Laboratory

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University of Illinois at Urbana-Champaign

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Jeffrey S. Vetter

Jeffrey S. Vetter

Oak Ridge National Laboratory

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Carl Tim Kelley

North Carolina State University

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Joaquim R. R. A. Martins

University of Michigan–Ann Arbor

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University of Colorado Denver

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George Biros

The University of Texas at Austin

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Manish Parashar

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