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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.

In this paper, we present the application of the GPU-based particle simulations to droplet-falling impact problem including free surfaces with surface tension and drag force. The particle approach is based on the MPS (Moving Particle Semi-implicit) method using logarithmic weighting functions. We adopt the inter-particle potential force model with a potential coefficient as a surface tension model. The GPU-implementation consists mainly of the search for neighboring particles in the locally uniform grid cell using hash function and solving the Poisson equation with respect to the pressure fields. Numerical results demonstrate the workability and validity of the present approach through the droplet-falling impact with surface tension and drag force.