Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs). Such conditions are usually imposed by adding penalization terms in the loss function and properly choosi...

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Detalles Bibliográficos
Autores principales: Berrone, S., Canuto, C., Pintore, M., Sukumar, N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432987/
https://www.ncbi.nlm.nih.gov/pubmed/37600384
http://dx.doi.org/10.1016/j.heliyon.2023.e18820

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432987/
https://www.ncbi.nlm.nih.gov/pubmed/37600384
http://dx.doi.org/10.1016/j.heliyon.2023.e18820

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