An improved data-free surrogate model for solving partial differential equations using deep neural networks

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to the need for tedious mesh generation and numerical iterations. Recently, deep neural networks have sho...

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Detalles Bibliográficos
Autores principales: Chen, Xinhai, Chen, Rongliang, Wan, Qian, Xu, Rui, Liu, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484684/
https://www.ncbi.nlm.nih.gov/pubmed/34593943
http://dx.doi.org/10.1038/s41598-021-99037-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484684/
https://www.ncbi.nlm.nih.gov/pubmed/34593943
http://dx.doi.org/10.1038/s41598-021-99037-x

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