Classifying modeling and simulation as a scientific discipline

The body of knowledge related to modeling and simulation (M&S) comes from a variety of constituents: (1) practitioners and users, (2) tool developers and (3) theorists and methodologists. Previous work has shown that categorizing M&S as a concentration in an existing, broader disciple is inadequate because it does not provide a uniform basis for research and education across all institutions. This article presents an approach for the classification of M&S as a scientific discipline and a framework for ensuing analysis. The novelty of the approach lies in its application of machine learning classification to documents containing unstructured text (e.g. publications, funding solicitations) from a variety of established and emerging disciplines related to modeling and simulation. We demonstrate that machine learning classification models can be trained to accurately separate M&S from related disciplines using the abstracts of well-index research publication repositories. We evaluate the accuracy of our trained classifiers using cross-fold validation. Then, we demonstrate that our trained classifiers can effectively identify a set of previously unseen M&S funding solicitations and grant proposals. Finally, we use our approach to uncover new funding trends in M&S and support a uniform basis for education and research.