9,625 research outputs found
Reveal flocking of birds flying in fog by machine learning
We study the first-order flocking transition of birds flying in
low-visibility conditions by employing three different representative types of
neural network (NN) based machine learning architectures that are trained via
either an unsupervised learning approach called "learning by confusion" or a
widely used supervised learning approach. We find that after the training via
either the unsupervised learning approach or the supervised learning one, all
of these three different representative types of NNs, namely, the
fully-connected NN, the convolutional NN, and the residual NN, are able to
successfully identify the first-order flocking transition point of this
nonequilibrium many-body system. This indicates that NN based machine learning
can be employed as a promising generic tool to investigate rich physics in
scenarios associated to first-order phase transitions and nonequilibrium
many-body systems.Comment: 7 pages, 3 figure
Particle diode: Rectification of interacting Brownian ratchets
Transport of Brownian particles interacting with each other via the Morse
potential is investigated in the presence of an ac driving force applied
locally at one end of the chain. By using numerical simulations, we find that
the system can behave as a particle diode for both overdamped and underdamped
cases. For low frequencies, the transport from the free end to the ac acting
end is prohibited, while the transport from the ac acting end to the free end
is permitted. However, the polarity of the particle diode will reverse for
medium frequencies. There exists an optimal value of the well depth of the
interaction potential at which the average velocity takes its maximum. The
average velocity decreases monotonically with the system size by
a power law .Comment: 7 pages, 9 figure
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