2023
DOI: 10.1016/j.heliyon.2023.e18820
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 42 publications
(54 reference statements)
0
1
0
Order By: Relevance
“…This modification is reflected in the second-to-last term in Equation (17). We further implement physical constraints by introducing a boundary loss, which is a standard implementation in many PINN architectures (Raissi et al 2019;Berrone et al 2023) and helps to satisfy the required boundary conditions. In this particular geometry for RBC, periodicity along the x-axis and constant temperatures on the top and bottom plates in the model-predicted output are enforced.…”
Section: This Study's Architectural Modificationsmentioning
confidence: 99%
“…This modification is reflected in the second-to-last term in Equation (17). We further implement physical constraints by introducing a boundary loss, which is a standard implementation in many PINN architectures (Raissi et al 2019;Berrone et al 2023) and helps to satisfy the required boundary conditions. In this particular geometry for RBC, periodicity along the x-axis and constant temperatures on the top and bottom plates in the model-predicted output are enforced.…”
Section: This Study's Architectural Modificationsmentioning
confidence: 99%