Akil Narayan

85 papers receiving 1.0k citations

Peers

Akil Narayan
Comparison fields: 5 of 96
  • Statistics, Probability and Uncertainty 562
  • Statistical and Nonlinear Physics 302
  • Computational Theory and Mathematics 236
  • Numerical Analysis 63
  • Computational Mechanics 230
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Eric Phipps United States
Bart van Bloemen Waanders United States
Aretha L. Teckentrup United Kingdom
Wen‐Juan Lin China
Xiaoliang Wan United States
Phaedon‐Stelios Koutsourelakis Germany
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Citations per field
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Citations per year

Countries citing papers authored by Akil Narayan

Since Specialization
Citations

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

Fields of papers citing papers by Akil Narayan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Akil Narayan, 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 Akil Narayan Line = papers co-authored together Akil Narayan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201485
2 201959
3 201252
4 201449
5 201645
6 201743
7 201941
8 201339
9 202336
10 201833
11 201831
12 201828
13 202025
14 201423
15 200121
16 202021
17 201820
18 201520
19 201919
20 202118

About Akil Narayan

Akil Narayan is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics, Computational Theory and Mathematics, Computational Mechanics and Civil and Structural Engineering, having authored 90 papers that have together received 1.1k indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (50 papers), Model Reduction and Neural Networks (24 papers), Advanced Multi-Objective Optimization Algorithms (16 papers), Structural Health Monitoring Techniques (11 papers), Cardiac electrophysiology and arrhythmias (10 papers), Scientific Research and Discoveries (8 papers), Numerical methods for differential equations (7 papers) and Fault Detection and Control Systems (6 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (562 citations), Statistical and Nonlinear Physics (302 citations), Computational Theory and Mathematics (236 citations), Numerical Analysis (63 citations) and Computational Mechanics (230 citations). Akil Narayan has collaborated with scholars based in United States, China and Austria. Frequent co-authors include Dongbin Xiu, Tao Tang, John Jakeman, Robert M. Kirby, Claude Jeffrey Gittelson, Ling Guo, Vahid Keshavarzzadeh, Yanlai Chen, Xueyu Zhu and Robert M. Kirby. Their work appears in journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, Journal of Scientific Computing, Computer Methods in Applied Mechanics and Engineering and SIAM/ASA Journal on Uncertainty Quantification.

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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