Complementary, Not Cumulative: Interaction Effects in Physics-Informed Neural Networks for Navier-Stokes Vortex Shedding
This paper demonstrates that while individual techniques often fail to improve Physics-Informed Neural Networks (PINNs) for unsteady Navier-Stokes flows, a specific complementary combination of periodic activations and causal weighting achieves high accuracy, whereas adding further methods leads to catastrophic performance degradation, proving that PINN interventions interact nonlinearly rather than cumulatively.
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