DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows
We present the deep random vortex network (DRVN), a novel physics-informed framework
for simulating and inferring the fluid dynamics governed by the incompressible Navier–
Stokes equations. Unlike the existing physics-informed neural network (PINN), which
embeds physical and geometry information through the residual of equations and boundary
data, DRVN automatically embeds this information into neural networks through neural
random vortex dynamics equivalent to the Navier–Stokes equation. Specifically, the neural …
for simulating and inferring the fluid dynamics governed by the incompressible Navier–
Stokes equations. Unlike the existing physics-informed neural network (PINN), which
embeds physical and geometry information through the residual of equations and boundary
data, DRVN automatically embeds this information into neural networks through neural
random vortex dynamics equivalent to the Navier–Stokes equation. Specifically, the neural …
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