  By using the supervised learning we train a recurrent neural network to
recognize and classify ultrafast magnetization processes realized in
two-dimensional nanosystems with Dzyaloshinskii-Moriya interaction. Our focus
is on the different types of skyrmion dynamics driven by ultrafast magnetic
pulses. Each process is represented as a sequence of the sorted magnetization
vectors inputted into the network. The trained network can perform an accurate
classification of the skyrmionic processes at zero temperature in wide ranges
of parameters that are the magnetic pulse width and damping factor. The network
performance is also demonstrated on different types of unseen data including
finite temperature processes. Our approach can be easily adapted for creating
an autonomous control system on skyrmion dynamics for experiments or device
operations.
