InSituNet : Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O a...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1998. - 26(2020), 1 vom: 19. Jan., Seite 23-33
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| Auteur principal: |
He, Wenbin
(Auteur) |
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| Autres auteurs: |
Wang, Junpeng,
Guo, Hanqi,
Wang, Ko-Chih,
Shen, Han-Wei,
Raj, Mukund,
Nashed, Youssef S G,
Peterka, Tom |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2020
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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