Ryan King

1.6k citations
51 papers · 931 indexed · h-index 16

Impact in

Papers in

Ryan King

51 papers receiving 898 citations

Peers

Ryan King
Comparison fields: 5 of 77
  • Environmental Engineering 249
  • Aerospace Engineering 362
  • Computational Mechanics 241
  • Statistical and Nonlinear Physics 133
  • Statistics, Probability and Uncertainty 59
Replace Kamyar Azizzadenesheli with:
Kamyar Azizzadenesheli United States
Edward Luke United States
Thomas D. Economon United States
Trent Lukaczyk United States
Song Chen China
Kyriakos C. Giannakoglou Greece
Matteo Diez Italy
Zhenghong Gao China
Jeffrey P. Slotnick United States
M. Nørgaard Denmark
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Citations per field
00.5×6.8×
Kamyar Azizzadenesheli · 1×
Citations per year

Countries citing papers authored by Ryan King

Since Specialization
Citations

This map shows the geographic impact of Ryan 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 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 King more than expected).

Fields of papers citing papers by Ryan King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 202414
2 202413
3 20242
4 202325
5 20223
6 20224
7 202115
8 202178
9 20215
10 202113
11 202030
12
Enabling predictive reduced order modeling of high-fidelity wind plant simulations with in-situ modal decomposition and basis interpolation
20181
13 20182
14 201815
15 201810
16
Creating Turbulent Flow Realizations with Generative Adversarial Networks
20173
17 201747
18 20162
19 201619
20 200250

About Ryan King

Ryan King is a scholar working on Statistics, Probability and Uncertainty, Environmental Engineering, Aerospace Engineering, Statistical and Nonlinear Physics and Computational Mechanics, having authored 51 papers that have together received 931 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 (11 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 (4 papers). The work is most often cited by research in Environmental Engineering (249 citations), Aerospace Engineering (362 citations), Computational Mechanics (241 citations), Statistical and Nonlinear Physics (133 citations) and Statistics, Probability and Uncertainty (59 citations). Ryan King has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Andrew Glaws, Katherine Dykes, Dylan Hettinger, Peter E. Hamlington, Mark Coates, Yolanda Tsang, Robert Nowak, Rui Castro, Peter Gräf and Paul Fleming. Their work appears in journals such as Wind energy science, Wind Energy, Journal of Energy Storage, Computational Mechanics and ACM SIGMETRICS Performance Evaluation Review.

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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