Comparison between conventional and deep learning-based surrogate models in predicting convective heat transfer performance of U-bend channels
Deep neural networks are efficient methods to achieve real-time visualization of physics fields. The main concerns that prevented deep learning from being implemented in the field of energy conversion were the risks of overfitting and the lack of data. Therefore, it is necessary to evaluate differen...
| Główni autorzy: | , , , |
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| Format: | Artykuł |
| Język: | English |
| Wydane: |
Elsevier
2022-05-01
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| Seria: | Energy and AI |
| Hasła przedmiotowe: | |
| Dostęp online: | http://www.sciencedirect.com/science/article/pii/S2666546822000040 |