Shift and flip invariant CNNs for predicting laminar flow properties
The integration of machine learning into fluid dynamics has accelerated in recent years, driven by the proliferation of high-fidelity data and enhanced computational resources. Acting as efficient surrogate models for computationally intensive simulations, these data-driven approaches provide substa...
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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
AIP Publishing LLC
2026-03-01
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| coleção: | APL Machine Learning |
| Acesso em linha: | https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0317297/20942525/016109_1_5.0317297.pdf |
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