Helicity-Preserving Physics-Informed Neural Network Model for Incompressible Navier-Stokes Equations

The core message of this paper is to design a helicity-conservative physics-informed neural network (PINN) model for solving the incompressible Navier-Stokes equations, which can exactly preserve the fluid helicity without any discretization error.