Combining finite-difference PINNs with warm-started domain decomposition reproduces the lid-driven cavity corner vortices at Re=400 and Re=1000 without training on known solutions.
Velocity–pressure coupling in finite difference formulations for the Navier–Stokes equations,
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Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equations
Combining finite-difference PINNs with warm-started domain decomposition reproduces the lid-driven cavity corner vortices at Re=400 and Re=1000 without training on known solutions.