Computational Scientific Discovery

Over the past decade, most of my discovery research has focused on a new framework, inductive process modeling, that combines background knowledge in the form of generic processes with time-series data to construct explanatory models stated as sets of differential equations. The basic approach carries out exhaustive search through a space of model structures followed by gradient descent through the parameter space for each candidate structure. Later work extended the framework to use constraints among processes to guide search through the structure space and even to induce constraints to discriminate between successful and unsuccessful structures.