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Learning data-driven discretizations for partial differential equations
The numerical solution of partial differential equations (PDEs) is challenging because of the need to resolve spatiotemporal features over wide length- and timescales. Often, it is computationally intractable to resolve the finest features in the solution. The only recourse is to use approximate coa...
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| Publicado no: | Proc Natl Acad Sci U S A |
|---|---|
| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
National Academy of Sciences
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6681734/ https://ncbi.nlm.nih.gov/pubmed/31311866 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1814058116 |
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