  This paper deals with an optimal control problem associated to the Kuramoto
model describing the dynamical behavior of a network of coupled oscillators.
Our aim is to design a suitable control function allowing us to steer the
system to a synchronized configuration in which all the oscillators are aligned
on the same phase. This control is computed via the minimization of a given
cost functional associated with the dynamics considered. For this minimization,
we propose a novel approach based on the combination of a standard Gradient
Descent (GD) methodology with the recently-developed Random Batch Method (RBM)
for the efficient numerical approximation of collective dynamics. Our
simulations show that the employment of RBM improves the performances of the GD
algorithm, reducing the computational complexity of the minimization process
and allowing for a more efficient control calculation.
