Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem

Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differ...

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Detalles Bibliográficos
Autores principales: Mandl, Luis, Mielke, André, Seyedpour, Seyed Morteza, Ricken, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511457/
https://www.ncbi.nlm.nih.gov/pubmed/37730743
http://dx.doi.org/10.1038/s41598-023-42141-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511457/
https://www.ncbi.nlm.nih.gov/pubmed/37730743
http://dx.doi.org/10.1038/s41598-023-42141-x

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