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.
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 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.
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.
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.
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Jacobian-free Newton-Krylov methods: a survey of approaches and applications
D. A. Knoll;D. E. Keyes.
Journal of Computational Physics (2004)
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)
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)
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)
Numerical Solution of Two-Dimensional Axisymmetric Laminar Diffusion Flames
M. D. Smooke;R. E. Mitchell;D. E. Keyes.
Combustion Science and Technology (1986)
Convergence Analysis of Pseudo-Transient Continuation
C. T. Kelley;David E. Keyes.
SIAM Journal on Numerical Analysis (1998)
Nonlinearly Preconditioned Inexact Newton Algorithms
Xiao-Chuan Cai;David E. Keyes.
SIAM Journal on Scientific Computing (2002)
High-performacne parallel implicit CFD
William D. Gropp;Dinesh K. Kaushik;David E. Keyes;Barry F. Smith.
parallel computing (2001)
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)
Large-Scale Inverse Problems and Quantification of Uncertainty
Lorenz Biegler;George Biros;Omar Nabih Ghattas;Matthias Heinkenschloss.
(2010)
Profile was last updated on December 6th, 2021.
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