Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Modal Analysis of Fluid Flows: An Overview
20171.2k citationsKunihiko Taira, Steven L. Brunton et al.AIAA Journalprofile →
Model Reduction for Flow Analysis and Control
2016509 citationsClarence W. Rowley, Scott T. M. DawsonAnnual Review of Fluid Mechanicsprofile →
Modal Analysis of Fluid Flows: Applications and Outlook
2019406 citationsKunihiko Taira, Maziar S. Hemati et al.AIAA Journalprofile →
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
202467 citationsScott T. M. Dawson, Ricardo Vinuesa et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Scott T. M. Dawson
Since
Specialization
Citations
This map shows the geographic impact of Scott T. M. Dawson'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 Scott T. M. Dawson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott T. M. Dawson more than expected).
Fields of papers citing papers by Scott T. M. Dawson
This network shows the impact of papers produced by Scott T. M. Dawson. 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 Scott T. M. Dawson. The network helps show where Scott T. M. Dawson may publish in the future.
Co-authorship network of co-authors of Scott T. M. Dawson
This figure shows the co-authorship network connecting the top 25 collaborators of Scott T. M. Dawson.
A scholar is included among the top collaborators of Scott T. M. Dawson 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 Scott T. M. Dawson. Scott T. M. Dawson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
D’Souza, Roshan M., et al.. (2020). Hemodynamic data assimilation using model order reduction and Kalman filter. Bulletin of the American Physical Society.2 indexed citations
10.
Dawson, Scott T. M., et al.. (2019). Reduced order modeling of pulsatile blood flow: multistage dynamic mode decomposition with control. Bulletin of the American Physical Society.2 indexed citations
11.
Taira, Kunihiko, Maziar S. Hemati, Steven L. Brunton, et al.. (2019). Modal Analysis of Fluid Flows: Applications and Outlook. AIAA Journal. 58(3). 998–1022.406 indexed citations breakdown →
Taira, Kunihiko, Steven L. Brunton, Scott T. M. Dawson, et al.. (2017). Modal Analysis of Fluid Flows: An Overview. AIAA Journal. 55(12). 4013–4041.1234 indexed citations breakdown →
14.
Rowley, Clarence W. & Scott T. M. Dawson. (2017). Modal analysis of fluid flows using variants of proper orthogonal decomposition. Bulletin of the American Physical Society.1 indexed citations
Rowley, Clarence W. & Scott T. M. Dawson. (2016). Model Reduction for Flow Analysis and Control. Annual Review of Fluid Mechanics. 49(1). 387–417.509 indexed citations breakdown →
Dawson, Scott T. M., Donald Rapp, Paul Sharps, et al.. (2003). Solar array development for the surface of Mars. World Conference on Photovoltaic Energy Conversion. 1. 789–792.4 indexed citations
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.