# Recurrent network classifier for ultrafast skyrmion dynamics

## TL;DR

This paper presents a recurrent neural network trained via supervised learning to classify ultrafast skyrmion dynamics in two-dimensional magnetic systems, enabling accurate recognition of different magnetization processes.

## Contribution

The study introduces a novel application of recurrent neural networks for classifying ultrafast skyrmion dynamics, including unseen data and finite temperature effects.

## Key findings

- High classification accuracy across parameter ranges
- Effective on unseen data including finite temperature processes
- Potential for autonomous control in skyrmion-based devices

## Abstract

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.