On the evolution of research in hypersonics: application of natural language processing and machine learning

TL;DR

This paper uses natural language processing and machine learning to analyze two decades of hypersonics research publications, identifying key themes and their evolution to better understand the research landscape.

Contribution

It introduces an automated, data-driven approach to characterize hypersonics research trends and themes over time, removing subjectivity from traditional review methods.

Findings

Identified 12 key latent research themes.

Detected cyclical patterns in research activity over 20 years.

Provided a comprehensive, objective analysis of research evolution.

Abstract

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.