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Physics-informed deep-learning applications to experimental fluid mechanics

Overview of attention for article published in Measurement Science & Technology, April 2024
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
58 Mendeley
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Title
Physics-informed deep-learning applications to experimental fluid mechanics
Published in
Measurement Science & Technology, April 2024
DOI 10.1088/1361-6501/ad3fd3
Authors

Hamidreza Eivazi, Yuning Wang, Ricardo Vinuesa

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 14%
Student > Master 7 12%
Lecturer 5 9%
Researcher 4 7%
Other 3 5%
Other 7 12%
Unknown 24 41%
Readers by discipline Count As %
Engineering 25 43%
Computer Science 3 5%
Energy 2 3%
Unspecified 1 2%
Materials Science 1 2%
Other 1 2%
Unknown 25 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2024.
All research outputs
#7,478,272
of 26,393,142 outputs
Outputs from Measurement Science & Technology
#863
of 3,319 outputs
Outputs of similar age
#95,162
of 339,135 outputs
Outputs of similar age from Measurement Science & Technology
#1
of 23 outputs
Altmetric has tracked 26,393,142 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 3,319 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,135 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.