Neural Network Analysis of the Magnetization Reversal in Magnetic Dot Arrays

We simulated the remagnetization dynamics of the ultra-dense and ultra-thin magnetic dot array system with dipole-dipole and exchange coupling interactions. Within the proposed 2D XY superlattice model, the square dots are modeled by the spatially modulated exchange-couplings. The dipole-dipole interactions were approximated by the hierarchical sums and dynamics was reduced to damping term of the Landau-Lifshitz-Gilbert equation. The simulation of 40000 spin system leads to nonequilibrium nonuniform configurations with soliton-antisoliton pairs detected at intra-dot and inter-dot scales. The classification of intra-dot magnetic configurations was performed using the self-adaptive neural networks with varying number of neurons.