A progressive Euler-PINN with geometry-aware loss weighting achieves CFD-comparable pressure and velocity fields for ten NACA6 blades across 30 operating points while cutting computational cost for family-wide screening.
Physics- informed neural networks: A deep learning framework for solving for- ward and inverse problems involving nonlinear partial differential equa- tions.J
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
physics.flu-dyn 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades
A progressive Euler-PINN with geometry-aware loss weighting achieves CFD-comparable pressure and velocity fields for ten NACA6 blades across 30 operating points while cutting computational cost for family-wide screening.