Steady Flow Approximation using Capsule Neural Networks
CFD (Computational Fluid Dynamics) solvers have been very popular for fluid flow simulation which has been proved to be imperative to solve modern problems relating to analysis, design, and optimization in the field of aerodynamics. Nevertheless, CFD simulations are usually memory intensive and computationally demanding, iterative time consuming processes. Such drawbacks often affect productivity and limit the design space exploration and forbid interactive design. The real time prediction of fluid flow helps us to overcome these drawbacks. There have been many successful implementations of the application of Deep Neural Networks for the fluid flow prediction. Especially until now, CNNs (Convolutional Neural Networks) have been proven to be cutting edge solution for such approximations. However, CNN poses some challenges. We propose a new fluid flow approximation model for prediction of velocity field in non uniform steady laminar flow based on Capsule Neural Networks.
2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)