Robert M. Kirby

70 papers receiving 1.7k citations

Hit Papers

Nektar++: An open-source spectral/ h p element framework20152026201820222015100200300

Peers

Robert M. Kirby
Comparison fields: 5 of 115
  • Computational Mechanics 952
  • Computer Vision and Pattern Recognition 223
  • Statistical and Nonlinear Physics 207
  • Electrical and Electronic Engineering 201
  • Computational Theory and Mathematics 174
Replace Carsten Burstedde with:
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Robert M. Kirby relative to Carsten Burstedde United States Carsten Burstedde's profile →
Citations per field
00.5×1.7×
Carsten Burstedde · 1×
Citations per year

Countries citing papers authored by Robert M. Kirby

Since Specialization
Citations

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

Fields of papers citing papers by Robert M. Kirby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert M. Kirby. 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 Robert M. Kirby. The network helps show where Robert M. Kirby may publish in the future.

Co-authorship network of co-authors of Robert M. Kirby

This figure shows the co-authorship network connecting the top 25 collaborators of Robert M. Kirby. A scholar is included among the top collaborators of Robert M. Kirby based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Robert M. Kirby. Robert M. Kirby is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
2
3
Scalable High-Order Gaussian Process Regression
8
4 28
5 2
6 6
7
Improving accuracy in particle methods using null spaces and filters
5
8 35
9 142
10 7
11 35
12 14
13 36
14 11
15 24
16 5
17 36
18 24
19 45
20 10

About Robert M. Kirby

Robert M. Kirby is a scholar working on Computer Graphics and Computer-Aided Design, Computational Mechanics and Statistics, Probability and Uncertainty, having authored 70 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Numerical Methods in Computational Mathematics (23 papers), Computational Fluid Dynamics and Aerodynamics (14 papers) and Model Reduction and Neural Networks (11 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (171 citations), Computational Mechanics (952 citations) and Statistical and Nonlinear Physics (207 citations). Robert M. Kirby has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Spencer J. Sherwin, Mahsa Mirzargar, Ross Whitaker, Jennifer K. Ryan, Chris D. Cantwell, Peter Vos, S. L. Yakovlev, Claes Eskilsson, Paul H. J. Kelly and David Moxey. Their work appears in journals such as Journal of Fluid Mechanics, Journal of Computational Physics and Communications of the ACM.

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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