77,968 research outputs found
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely
qualitative, but with bibliometric data it is possible and desirable to develop
a quantitative picture of scientific progress. Furthermore it is also important
to allocate finite resources to research topics that have growth potential, to
accelerate the process from scientific breakthroughs to technological
innovations. In this paper, we address this problem of quantitative knowledge
evolution by analysing the APS publication data set from 1981 to 2010. We build
the bibliographic coupling and co-citation networks, use the Louvain method to
detect topical clusters (TCs) in each year, measure the similarity of TCs in
consecutive years, and visualize the results as alluvial diagrams. Having the
predictive features describing a given TC and its known evolution in the next
year, we can train a machine learning model to predict future changes of TCs,
i.e., their continuing, dissolving, merging and splitting. We found the number
of papers from certain journals, the degree, closeness, and betweenness to be
the most predictive features. Additionally, betweenness increases significantly
for merging events, and decreases significantly for splitting events. Our
results represent a first step from a descriptive understanding of the Science
of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include
Learning to Predict the Cosmological Structure Formation
Matter evolved under influence of gravity from minuscule density
fluctuations. Non-perturbative structure formed hierarchically over all scales,
and developed non-Gaussian features in the Universe, known as the Cosmic Web.
To fully understand the structure formation of the Universe is one of the holy
grails of modern astrophysics. Astrophysicists survey large volumes of the
Universe and employ a large ensemble of computer simulations to compare with
the observed data in order to extract the full information of our own Universe.
However, to evolve trillions of galaxies over billions of years even with the
simplest physics is a daunting task. We build a deep neural network, the Deep
Density Displacement Model (hereafter DM), to predict the non-linear
structure formation of the Universe from simple linear perturbation theory. Our
extensive analysis, demonstrates that DM outperforms the second order
perturbation theory (hereafter 2LPT), the commonly used fast approximate
simulation method, in point-wise comparison, 2-point correlation, and 3-point
correlation. We also show that DM is able to accurately extrapolate far
beyond its training data, and predict structure formation for significantly
different cosmological parameters. Our study proves, for the first time, that
deep learning is a practical and accurate alternative to approximate
simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl
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