Solving Navier-Stokes equations with mixed equation physics informed neural networks [Navier-Stokes denklemlerinin karma denklemli fizik bilgili nöral ağlarla çözümü]

Official URL: http://dx.doi.org/10.1109/SIU59756.2023.10223799

This paper presents a study on the implementation and testing of mixed-precision and mixed-equation approaches for optimizing the performance of physics-informed neural networks. Mixed-equation approach involves utilizing equations in a multi-step manner, which leads to a significant reduction in computational costs during the network's training while capturing complex physical phenomena. Specifically, we demonstrate the effectiveness of the proposed methodology in approximating the Navier-Stokes equations for incompressible flow around a 2D cylinder.