This stiff, coupled system is expensive to solve with finite-difference techniques. Therefore, we are motivated to use neural networks because of their potential to solve these equations faster than conventional methods. Neural networks are trained by example, not mathematical properties, and utilize parallel distributed processing to reach a solution.
Information is stored in the network in the form of connections between nonlinear elements.
We optimized our neural network to solve this problem efficiently and effectively. This project represents a collaborative effort between Earth Tech, an environmental consulting firm, and the undergraduate mathematics department at the University of Massachusetts at Lowell.