Effect of network architecture on physics-informed deep learning of the Reynolds-averaged turbulent flow field around cylinders without training data
Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. Ho...
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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Frontiers Media S.A.
2024-03-01
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| Seri Bilgileri: | Frontiers in Physics |
| Konular: | |
| Online Erişim: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1385381/full |
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