John Red-Horse

39 papers receiving 886 citations

Peers

John Red-Horse
Comparison fields: 5 of 82
  • Statistics, Probability and Uncertainty 710
  • Civil and Structural Engineering 407
  • Environmental Engineering 190
  • Statistical and Nonlinear Physics 180
  • Computational Theory and Mathematics 166
Replace K. Sobczyk with:
K. Sobczyk Poland
Sai Hung Cheung Singapore
Jerrad Hampton United States
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Citations per field
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Citations per year

Countries citing papers authored by John Red-Horse

Since Specialization
Citations

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

Fields of papers citing papers by John Red-Horse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Red-Horse. 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 John Red-Horse. The network helps show where John Red-Horse may publish in the future.

Co-authorship network of co-authors of John Red-Horse

This figure shows the co-authorship network connecting the top 25 collaborators of John Red-Horse. A scholar is included among the top collaborators of John Red-Horse 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 John Red-Horse. John Red-Horse 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 14
2 15
3
Measure transformation and efficient quadrature in reduced-dimensional stochastic analysis of coupled systems
1
4 6
5 48
6 1
7 142
8 117
9 45
10 20
11 1
12
A probabilistic approach to uncertainty quantification with limited information.
1
13
Statistical tests of system linearity based on the method of surrogate data
1
14
Uncertainty evaluation in dynamic system response
2
15 6
16 4
17
System identification of the JPL Micro-Precision Interferometer truss. A overview
1
18 21
19 9
20 0

About John Red-Horse

John Red-Horse is a scholar working on Statistics, Probability and Uncertainty, Civil and Structural Engineering and Statistical and Nonlinear Physics, having authored 40 papers that have together received 943 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (26 papers), Structural Health Monitoring Techniques (16 papers) and Wind and Air Flow Studies (8 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (710 citations), Civil and Structural Engineering (407 citations) and Statistical and Nonlinear Physics (180 citations). John Red-Horse has collaborated with scholars based in United States, Belgium and Saudi Arabia. Frequent co-authors include Roger Ghanem, Pol D. Spanos, Alireza Doostan, Michael Beer, T.L. Paez, Debraj Ghosh, Richard Field, Maarten Arnst, Thomas L. Paez and Eric Phipps. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, AIAA Journal and International Journal for Numerical Methods in 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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