Liquid Splash Modeling with Neural Networks

Liquid Splash Modeling with Neural Networks

This paper proposes a new data-driven approach for modeling detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for uid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations. We use neural networks to model the regression of splash formation using a classi er together with a ...

Accelerated Animation of Liquid Splash

We present a method for simulation and visualization of a liquid splash motion. The proposed system is composed of a computational fluid dynamics and a polygon based renderer using multi texturing graphical acceleration. The system well supports the NavierStokes solver with adaptive time step and viscosity. The final liquid surface is obtained from the velocity field by rough estimation of the ...

Modeling with Spiking Neural Networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific applicat...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)

Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...