Deep learning approaches in flow visualization
Abstract With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-04-01
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| Series: | Advances in Aerodynamics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42774-022-00113-1 |
| _version_ | 1811303004098265088 |
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| author | Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan |
| author_facet | Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan |
| author_sort | Can Liu |
| collection | DOAJ |
| description | Abstract With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning. |
| first_indexed | 2024-04-13T07:40:32Z |
| format | Article |
| id | doaj.art-ba47c938087f4f078db6c2c36f5f86d3 |
| institution | Directory Open Access Journal |
| issn | 2524-6992 |
| language | English |
| last_indexed | 2024-04-13T07:40:32Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Advances in Aerodynamics |
| spelling | doaj.art-ba47c938087f4f078db6c2c36f5f86d32022-12-22T02:55:56ZengSpringerOpenAdvances in Aerodynamics2524-69922022-04-014111410.1186/s42774-022-00113-1Deep learning approaches in flow visualizationCan Liu0Ruike Jiang1Datong Wei2Changhe Yang3Yanda Li4Fang Wang5Xiaoru Yuan6Key Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityKey Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityKey Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityKey Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityKey Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityChina Aerodynamics Research and Development CenterKey Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking UniversityAbstract With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.https://doi.org/10.1186/s42774-022-00113-1Deep learningFlow visualizationData managementFeature extractionParticle tracing |
| spellingShingle | Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Deep learning approaches in flow visualization Advances in Aerodynamics Deep learning Flow visualization Data management Feature extraction Particle tracing |
| title | Deep learning approaches in flow visualization |
| title_full | Deep learning approaches in flow visualization |
| title_fullStr | Deep learning approaches in flow visualization |
| title_full_unstemmed | Deep learning approaches in flow visualization |
| title_short | Deep learning approaches in flow visualization |
| title_sort | deep learning approaches in flow visualization |
| topic | Deep learning Flow visualization Data management Feature extraction Particle tracing |
| url | https://doi.org/10.1186/s42774-022-00113-1 |
| work_keys_str_mv | AT canliu deeplearningapproachesinflowvisualization AT ruikejiang deeplearningapproachesinflowvisualization AT datongwei deeplearningapproachesinflowvisualization AT changheyang deeplearningapproachesinflowvisualization AT yandali deeplearningapproachesinflowvisualization AT fangwang deeplearningapproachesinflowvisualization AT xiaoruyuan deeplearningapproachesinflowvisualization |