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Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems
Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning t...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689563/ https://www.ncbi.nlm.nih.gov/pubmed/38046086 http://dx.doi.org/10.1186/s40323-023-00254-y |