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

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 sp...

Descripción completa

Detalles Bibliográficos
Autores principales: Palmucci, Andrea, Liao, Hao, Napoletano, Andrea, Zaccaria, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302634/
https://www.ncbi.nlm.nih.gov/pubmed/32555661
http://dx.doi.org/10.1371/journal.pone.0233997
_version_ 1783547889379704832
author Palmucci, Andrea
Liao, Hao
Napoletano, Andrea
Zaccaria, Andrea
author_facet Palmucci, Andrea
Liao, Hao
Napoletano, Andrea
Zaccaria, Andrea
author_sort Palmucci, Andrea
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7302634
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-73026342020-06-19 Where is your field going? A machine learning approach to study the relative motion of the domains of physics Palmucci, Andrea Liao, Hao Napoletano, Andrea Zaccaria, Andrea PLoS One Research Article 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. Public Library of Science 2020-06-18 /pmc/articles/PMC7302634/ /pubmed/32555661 http://dx.doi.org/10.1371/journal.pone.0233997 Text en © 2020 Palmucci et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Palmucci, Andrea
Liao, Hao
Napoletano, Andrea
Zaccaria, Andrea
Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title_full Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title_fullStr Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title_full_unstemmed Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title_short Where is your field going? A machine learning approach to study the relative motion of the domains of physics
title_sort where is your field going? a machine learning approach to study the relative motion of the domains of physics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302634/
https://www.ncbi.nlm.nih.gov/pubmed/32555661
http://dx.doi.org/10.1371/journal.pone.0233997
work_keys_str_mv AT palmucciandrea whereisyourfieldgoingamachinelearningapproachtostudytherelativemotionofthedomainsofphysics
AT liaohao whereisyourfieldgoingamachinelearningapproachtostudytherelativemotionofthedomainsofphysics
AT napoletanoandrea whereisyourfieldgoingamachinelearningapproachtostudytherelativemotionofthedomainsofphysics
AT zaccariaandrea whereisyourfieldgoingamachinelearningapproachtostudytherelativemotionofthedomainsofphysics

Ejemplares similares