Ben Johnson - Final Presentation.pdf

Machine learning is a rapidly developing field of computer science with broad applications, including computational physics. This project focused on training methods for a type of machine learning system known as a reservoir computer (RC). The objective of this project was to expand the performance of an RC trained on a physical system with a specific set of equation parameters to predict the behavior accurately for different parameter values. We considered three methods of determining RC output weight matrices for the modeling of physical systems described by differential equations (in this case, the Van der Pol and Lorenz equations). The first method involved averaging the output weight matrices produced autonomously by the RC during the training period by implementing a Gaussian weighting function. For the second method, we instead passed the equation parameter into the RC directly and allowed it to analyze the effect of the parameter value on the system’s behavior. In the third method, we trained a feed-forward neural network to map directly from parameter values to output weight matrices for the RC.