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.
A comprehensive review of advances in physics- informed neural networks and their applications in complex fluid dy- namics.Physics of Fluids, 36(10):101301
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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.