  We performed a numerical study to train smart inertial particles to target
specific flow regions with high vorticity through the use of reinforcement
learning algorithms. The particles are able to actively change their size to
modify their inertia and density. In short, using local measurements of the
flow vorticity, the smart particle explores the interplay between its choices
of size and its dynamical behaviour in the flow environment. This allows it to
accumulate experience and learn approximately optimal strategies of how to
modulate its size in order to reach the target high-vorticity regions. We
consider flows with different complexities: a two-dimensional stationary
Taylor-Green like configuration, a two-dimensional time-dependent flow, and
finally a three-dimensional flow given by the stationary
Arnold-Beltrami-Childress helical flow. We show that smart particles are able
to learn how to reach extremely intense vortical structures in all the tackled
cases.
