Where is your field going? A Machine Learning approach to study the relative motion of the domains of Physics

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.

References in corpus (11)

• A nonequilibrium equality for free energy differences
• word2vec Parameter Learning Explained
• A network approach to topic models
• Quantifying patterns of research interest evolution
• The Scientific Prize Network Predicts Who Pushes the Boundaries of Science
• StarSpace: Embed All The Things!
• Mapping the evolution of scientific fields
• Taking census of physics
• Investigating the interplay between fundamentals of national research systems: performance, investments and international collaborations
• Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster
• Scientometrics: Untangling the topics

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