Flow Field Reduction Via Reconstructing Vector Data From 3-D Streamlines Using Deep Learning
We present a new approach for streamline-based flow field representation and reduction. Our method can work in the in situ visualization setting by tracing streamlines from each time step of the simulation and storing compressed streamlines for post hoc analysis where users can afford longer reconst...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE computer graphics and applications. - 1997. - 39(2019), 4 vom: 25. Juli, Seite 54-67
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| 1. Verfasser: |
Han, Jun
(VerfasserIn) |
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| Weitere Verfasser: |
Tao, Jun,
Zheng, Hao,
Guo, Hanqi,
Chen, Danny Z,
Wang, Chaoli |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | IEEE computer graphics and applications
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| Schlagworte: | Journal Article |
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