Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data Article Swipe
Christian Lagemann
,
Kai Lagemann
,
Sach Mukherjee
,
Wolfgang Schröder
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4432495
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4432495
This is the official dataset of Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Data (RAFT-PIV) published in Nature Machine Intelligence. In this work, we propose a deep neural network-based approach for learning displacement fields in an end-to-end manner, focusing on the specific case of Particle Image Velocimetry (PIV). PIV is a key approach in experimental fluid dynamics and of fundamental importance in diverse applications, including automotive, aerospace, and biomedical engineering. In contrast to standard PIV methods, our RAFT-PIV approach is general, largely automated, and provides much higher spatial resolution. This dataset is given as binary TFRECORD format.
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- dataset
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.4432495
- OA Status
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- DOI
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https://doi.org/10.5281/zenodo.4432495Digital Object Identifier
- Title
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Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry DataWork title
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datasetOpenAlex work type
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enPrimary language
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2021Year of publication
- Publication date
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2021-01-11Full publication date if available
- Authors
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Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang SchröderList of authors in order
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https://doi.org/10.5281/zenodo.4432495Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://doi.org/10.5281/zenodo.4432495Direct OA link when available
- Concepts
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Particle image velocimetry, Particle tracking velocimetry, Velocimetry, Optical flow, Particle (ecology), Flow (mathematics), Artificial intelligence, Computer science, Geology, Image (mathematics), Physics, Optics, Meteorology, Mechanics, Oceanography, TurbulenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(RAFT-PIV) | 15 |
| abstract_inverted_index.Transforms | 9 |
| abstract_inverted_index.aerospace, | 68 |
| abstract_inverted_index.automated, | 84 |
| abstract_inverted_index.biomedical | 70 |
| abstract_inverted_index.end-to-end | 37 |
| abstract_inverted_index.importance | 62 |
| abstract_inverted_index.Velocimetry | 13, 47 |
| abstract_inverted_index.automotive, | 67 |
| abstract_inverted_index.fundamental | 61 |
| abstract_inverted_index.resolution. | 90 |
| abstract_inverted_index.displacement | 33 |
| abstract_inverted_index.engineering. | 71 |
| abstract_inverted_index.experimental | 56 |
| abstract_inverted_index.Intelligence. | 20 |
| abstract_inverted_index.applications, | 65 |
| abstract_inverted_index.network-based | 29 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |