Magnetic dot arrays modeling via the system of the radial basis function networks

TL;DR

This paper introduces a neural network-based model for simulating magnetic dot arrays, capturing intradot self-energy and interdot interactions, and applies it to a large disk-shaped cluster to analyze magnetic structures.

Contribution

It presents a novel neural network approach using radial basis function networks to model magnetic dot arrays, including intradot and interdot interactions, with application to a large-scale cluster.

Findings

Single-vortex magnetization modes are significant in intradot structures.

The model successfully predicts magnetic configurations using simulated annealing.

Neural networks effectively approximate complex magnetic energies.

Abstract

Two dimensional square lattice general model of the magnetic dot array is introduced. In this model the intradot self-energy is predicted via the neural network and interdot magnetostatic coupling is approximated by the collection of several dipolar terms. The model has been applied to disk-shaped cluster involving 193 ultrathin dots and 772 interaction centers. In this case among the intradot magnetic structures retrieved by neural networks the important role play single-vortex magnetization modes. Several aspects of the model have been understood numerically by means of the simulated annealing method.