Reduced order fluid modeling with generative adversarial networks
Abstract: Surrogate models based on convolutional neural networks (CNNs) for computational fluid dynamics (CFD) simulations are investigated. In particular, the flow field inside two‐dimensional channels with a sudden expansion and an obstacle is predicted using an image representation of the geometry as the input. Generative adversarial neural networks (GANs) have been shown to excel at such image‐to‐image translation tasks. This motivates the focus of this work on investigating the specific effect of adversarial training on model performance. Numerical results show that the overall accuracy of the GANs is generally lower compared to an identical generator model trained directly on the ground truth using an L1 data loss. On the other hand, GAN predictions are often visually more convincing and exhibit a lower continuity residual.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Reduced order fluid modeling with generative adversarial networks ; volume:23 ; number:1 ; year:2023 ; extent:6
Proceedings in applied mathematics and mechanics ; 23, Heft 1 (2023) (gesamt 6)
- Creator
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Kemna, Mirko
Heinlein, Alexander
Vuik, Cornelis
- DOI
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10.1002/pamm.202200241
- URN
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urn:nbn:de:101:1-2023060115124297738393
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:44 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kemna, Mirko
- Heinlein, Alexander
- Vuik, Cornelis
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