Ryan N. King

54 papers receiving 1.1k citations

Peers

Ryan N. King
Comparison fields: 5 of 82
  • Environmental Engineering 269
  • Aerospace Engineering 394
  • Statistical and Nonlinear Physics 152
  • Computational Mechanics 252
  • Statistics, Probability and Uncertainty 57
Replace EDWARD A. LUKE with:
EDWARD A. LUKE United States
Trent W. Lukaczyk United States
Kunshan Yang United States
Song Chen China
Matthias Wächter Germany
Magnus Nørgaard Denmark
Jens‐Dominik Müller United Kingdom
Kwanjung Yee South Korea
Matteo Diez Italy
Zhenghong Gao China
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Citations per year

Countries citing papers authored by Ryan N. King

Since Specialization
Citations

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

Fields of papers citing papers by Ryan N. King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan N. King. 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 Ryan N. King. The network helps show where Ryan N. King may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020173
2 2002137
3 202190
4 200260
5 201956
6 201751
7 201744
8 202040
9 201434
10 202032
11 202332
12 202025
13 202425
14 202425
15 202422
16 201621
17 201620
18 202220
19 201820
20 202419

About Ryan N. King

Ryan N. King is a scholar working on Environmental Engineering, Aerospace Engineering, Statistics, Probability and Uncertainty, Computational Mechanics and Statistical and Nonlinear Physics, having authored 57 papers that have together received 1.1k indexed citations. Recurring topics across this work include Wind Energy Research and Development (21 papers), Wind and Air Flow Studies (19 papers), Probabilistic and Robust Engineering Design (10 papers), Model Reduction and Neural Networks (9 papers), Fluid Dynamics and Turbulent Flows (8 papers), Fluid Dynamics and Vibration Analysis (8 papers), Advanced Multi-Objective Optimization Algorithms (6 papers) and Energy Load and Power Forecasting (6 papers). The work is most often cited by research in Environmental Engineering (269 citations), Aerospace Engineering (394 citations), Statistical and Nonlinear Physics (152 citations), Computational Mechanics (252 citations) and Statistics, Probability and Uncertainty (57 citations). Ryan N. King has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Andrew Glaws, Dylan Hettinger, Katherine Dykes, Peter E. Hamlington, Rui M. G. Castro, Mark Coates, Yolanda Tsang, Robert Marek Nowak, Peter A Graf and Paul A Fleming. Their work appears in journals such as Wind energy science, Wind Energy, Nature Energy, Journal of Energy Storage and Computing in Science & 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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