Where is your field going? A machine learning approach to study the relative motion of the domains of physics

2019/11/07 by Andrea Palmucci, Hao Liao, Andrea Napoletano +1 · 8 citations
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Artificial intelligence #Computer science #Convergence (economics) #Data Analysis with R #Data Visualization and Analytics #Data science #Field (mathematics) #Machine learning #Mathematics #Motion (physics) #Physics #Pure mathematics #Representation (politics) #Space (punctuation) #Theoretical physics #physics.soc-ph #scientometrics and bibliometrics research

paper · pdf · doi:10.1371/journal.pone.0233997

published in PLoS ONE 15(6), e0233997 (Public Library of Science)

arxiv created 2019/11/07 · openalex publication_date 2020/06/18 · arxiv updated 2020/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multi-dimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields.

Citations

• Bose-Einstein Condensation in a Gas of Sodium Atoms
• The meaning and use of the area under a receiver operating characteristic (ROC) curve.
• An introduction to ROC analysis
• Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster
• A network approach to topic models
• StarSpace: Embed All The Things!