Artificial Neural Networks in Fluid Dynamics: A Novel Approach to the Navier-Stokes Equations
This is an incremental improvement for fluid dynamics researchers, applying a hybrid neural network method to a specific turbulence problem.
The paper tackled solving the Navier-Stokes equations for turbulence by training a neural network with Bayesian Cluster and SOM neighbor weighting to map ionospheric velocity fields, achieving 67% accuracy on validation data.
Neural networks have been used to solve different types of large data related problems in many different fields.This project takes a novel approach to solving the Navier-Stokes Equations for turbulence by training a neural network using Bayesian Cluster and SOM neighbor weighting to map ionospheric velocity fields based on 3-dimensional inputs. Parameters used in this problem included the velocity, Reynolds number, Prandtl number, and temperature. In this project data was obtained from Johns-Hopkins University to train the neural network using MATLAB. The neural network was able to map the velocity fields within a sixty-seven percent accuracy of the validation data used. Further studies will focus on higher accuracy and solving further non-linear differential equations using convolutional neural networks.