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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
|
|---|---|
| Published in |
Journal of Computational Physics, October 2023
|
| DOI | 10.1016/j.jcp.2023.112369 |
| Authors |
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 8 | 42% |
| United States | 2 | 11% |
| Czechia | 1 | 5% |
| France | 1 | 5% |
| India | 1 | 5% |
| Hungary | 1 | 5% |
| Unknown | 5 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 53% |
| Members of the public | 9 | 47% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 26% |
| Student > Master | 4 | 17% |
| Researcher | 2 | 9% |
| Librarian | 1 | 4% |
| Student > Bachelor | 1 | 4% |
| Other | 5 | 22% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 11 | 48% |
| Mathematics | 2 | 9% |
| Unspecified | 2 | 9% |
| Physics and Astronomy | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 6 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 08 August 2023.
All research outputs
#4,190,833
of 25,619,480 outputs
Outputs from Journal of Computational Physics
#116
of 5,783 outputs
Outputs of similar age
#64,128
of 358,402 outputs
Outputs of similar age from Journal of Computational Physics
#3
of 111 outputs
Altmetric has tracked 25,619,480 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,783 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done particularly well, scoring higher than 98% 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 358,402 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 111 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 98% of its contemporaries.