Sheehan Olver

2.0k citations
58 papers · 1.2k · h-index 21

Impact in

Papers in

Sheehan Olver

55 papers receiving 1.2k citations

Peers

Sheehan Olver
Comparison fields: 5 of 65
  • Numerical Analysis 374
  • Modeling and Simulation 286
  • Applied Mathematics 557
  • Computational Mathematics 13
  • Statistical and Nonlinear Physics 243
Replace Masaaki Sugihara with:
Masaaki Sugihara Japan
Martin H. Gutknecht Switzerland
Siegfried Prößdorf Germany
Daniele Funaro Italy
Marco Caliari Italy
Bernard Deconinck United States
Richard W. L. Wong Hong Kong
Erwan Faou France
Leonid Aronovich Knizhnerman United States
H. Sadok France
Sheehan Olver relative to Masaaki Sugihara Japan Masaaki Sugihara's profile →
Citations per field
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Masaaki Sugihara · 1×
Citations per year

Countries citing papers authored by Sheehan Olver

Since Specialization
Citations

This map shows the geographic impact of Sheehan Olver'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 Sheehan Olver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheehan Olver more than expected).

Fields of papers citing papers by Sheehan Olver

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheehan Olver. 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 Sheehan Olver. The network helps show where Sheehan Olver may publish in the future.

Co-authors

The 20 scholars most cited alongside Sheehan Olver, 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 Sheehan Olver Line = papers co-authored together Sheehan Olver links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005153
2 2013153
3 201572
4 200751
5 201547
6 200744
7 201044
8 201239
9 200739
10 201238
11 200936
12 201132
13 202032
14 201632
15 201030
16 200629
17 201428
18 201127
19 201525
20 201223

About Sheehan Olver

Sheehan Olver is a scholar working on Applied Mathematics, Numerical Analysis, Computational Theory and Mathematics, Modeling and Simulation and Statistical and Nonlinear Physics, having authored 58 papers that have together received 1.2k indexed citations. Recurring topics across this work include Mathematical functions and polynomials (34 papers), Matrix Theory and Algorithms (19 papers), Electromagnetic Scattering and Analysis (16 papers), Numerical methods for differential equations (13 papers), Nonlinear Waves and Solitons (10 papers), Fractional Differential Equations Solutions (10 papers), Iterative Methods for Nonlinear Equations (8 papers) and Random Matrices and Applications (4 papers). The work is most often cited by research in Numerical Analysis (374 citations), Modeling and Simulation (286 citations), Applied Mathematics (557 citations), Computational Mathematics (13 citations) and Statistical and Nonlinear Physics (243 citations). Sheehan Olver has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Alex Townsend, Thomas Trogdon, Daan Huybrechs, Yuan Xu, Bernard Deconinck, Arieh Iserles, Syvert P. Nørsett, Nicholas Hale, Geoffrey M. Vasil and José Antonio Carrillo. Their work appears in journals such as Mathematics of Computation, Foundations of Computational Mathematics, IMA Journal of Numerical Analysis, Journal of Computational Physics and Numerische Mathematik.

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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