The problem of energetic alpha particle deposition in a dense, magnetized deuterium-tritium (DT) thermonuclear fuel has been studied numerically for the case of coulomb interactions in cylindrical geometry. This was done by following the particle trajectories initiated at various radii and in different directions through the plasma and its imposed field until they had either left the plasma or deposited all their energy. The resulting complex particle trajectories in the static magnetized fuel make a detailed treatment of the problem computationally intensive. Therefore, we have attempted to use detailed modeling to produce a data base for a neural nets algorithm for incorporation in an ignition critical profile code. While the accuracy of the neural net in reproducing the detailed calculational results is not high, it is approximately 6000 times faster.