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.
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