  Research and development in hypersonics have progressed significantly in
recent years, with various military and commercial applications being
demonstrated increasingly. Public and private organizations in several
countries have been investing in hypersonics, with the aim to overtake their
competitors and secure/improve strategic advantage and deterrence. For these
organizations, being able to identify emerging technologies in a timely and
reliable manner is paramount. Recent advances in information technology have
made it possible to analyze large amounts of data, extract hidden patterns, and
provide decision-makers with new insights. In this study, we focus on
scientific publications about hypersonics within the period of 2000-2020, and
employ natural language processing and machine learning to characterize the
research landscape by identifying 12 key latent research themes and analyzing
their temporal evolution. Our publication similarity analysis revealed patterns
that are indicative of cycles during two decades of research. The study offers
a comprehensive analysis of the research field and the fact that the research
themes are algorithmically extracted removes subjectivity from the exercise and
enables consistent comparisons between topics and between time intervals.
