David Day

29 papers receiving 500 citations

Peers

David Day
Comparison fields: 5 of 62
  • Numerical Analysis 166
  • Computational Theory and Mathematics 298
  • Computational Mathematics 7
  • Computational Mechanics 204
  • Hardware and Architecture 46
Replace M. Arioli with:
M. Arioli United Kingdom
Reinhard Nabben Germany
Wayne Joubert United States
Miroslav Rozložńık Czechia
Petter E. Bjørstad Norway
Fang Chen China
Matthias Bollhöfer Germany
Jacob B. Schroder United States
Artem Napov Belgium
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Citations per field
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M. Arioli · 1×
Citations per year

Countries citing papers authored by David Day

Since Specialization
Citations

This map shows the geographic impact of David Day's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Day with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Day more than expected).

Fields of papers citing papers by David Day

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Day. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Day. The network helps show where David Day may publish in the future.

Co-authors

The 25 scholars most cited alongside David Day, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Day Line = papers co-authored together David Day links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200188
2 200072
3 199972
4
A Test Matrix Collection for Non-Hermitian Eigenvalue Problems
199760
5 200242
6 200237
7 200535
8 200927
9 200224
10 199719
11 200519
12
Semi-duality in the two-sided lanczos algorithm
19939
13 20009
14 20078
15 19925
16
New concept for lightning surge protection of avionics using 4H-SiC current limiter
20135
17 20115
18 20034
19 20044
20 20084

About David Day

David Day is a scholar working on Computational Theory and Mathematics, Numerical Analysis, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Control and Systems Engineering, having authored 31 papers that have together received 567 indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (12 papers), Numerical methods for differential equations (6 papers), Electromagnetic Scattering and Analysis (4 papers), Advanced Numerical Methods in Computational Mathematics (4 papers), Parallel Computing and Optimization Techniques (3 papers), Electromagnetic Simulation and Numerical Methods (3 papers), Vibration and Dynamic Analysis (3 papers) and Numerical Methods and Algorithms (3 papers). The work is most often cited by research in Numerical Analysis (166 citations), Computational Theory and Mathematics (298 citations), Computational Mathematics (7 citations), Computational Mechanics (204 citations) and Hardware and Architecture (46 citations). David Day has collaborated with scholars based in United States, France and Canada. Frequent co-authors include Michael A. Heroux, Manoj Bhardwaj, Qiang Ye, Zhaojun Bai, Michel Lesoinne, Charbel Farhat, Jack Dongarra, Louis A. Romero, Kendall Pierson and Garth Reese. Their work appears in journals such as SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, SIAM Journal on Numerical Analysis, International Journal for Numerical Methods in Engineering and Computer Methods in Applied Mechanics and Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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