  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.
