Tag: Aeroelasticity

CFD/Fluid

A periodic physics-informed neural network (P-PINN) framework is proposed for solving parametric periodic flows and analyzing finite-amplitude aeroelastic responses in a purely physics-driven manner.

CFD

Proposes p-FNM, a neural-operator framework using periodic Fourier mapping to predict unsteady pressure distributions and aeroelastic loads on turbine rotor blades, reducing high-fidelity CFD computational costs.