# Learning magnetization dynamics

## TL;DR

This paper introduces a deep learning approach using autoencoders and neural networks to efficiently model and simulate magnetization dynamics in magnetic thin films, significantly reducing computational complexity.

## Contribution

It presents a novel method combining autoencoders and neural networks to model magnetization dynamics in a low-dimensional space, enabling fast simulations.

## Key findings

- Achieved a 1024:1 compression ratio with autoencoders.
- Enabled rapid time integration in the latent space.
- Demonstrated accurate modeling of magnetic response to external fields.

## Abstract

Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional space. With convolutional autoencoders a compression ratio of 1024:1 was achieved. Time integration can be performed in the latent space with a second network which was trained by solutions of the Landau-Lifshitz-Gilbert equation. Thus the magnetic response to an external field can be computed quickly.