Data-Driven Fluid Flow Simulations by Using Convolutional Neural Network

In this paper, we present the data-driven fluid flow simulations using the deep CNN (Convolutional Neural Network) with the parametric softsign activation functions. To simulate the fluid flow problems, the particle-method approach based on SPH (Smoothed Particle Hydrodynamics) is used herein. The GPU-implementation consists mainly of the search for neighboring particles in the locally uniform grid cell using hash function. We construct significantly the deep CNN architectures with novel activation functions, so-called parametric softsign. Numerical results demonstrate the workability and validity of the present approach through the dam-breaking fluid flow simulations with free surface.