Neural network approach to magnetic dot arrays modeling

Abstract

Two-dimensional square lattice model of the magnetic dot array is presented. The intradot self-energy is predicted using the neural network approachand interdot magnetostatic coupling is approximated by the several dipolar terms. The model is applied to disk-shaped cluster involving 193 ultrathin square dots with 772 interaction centers. Among the intradot magnetic structures retrieved by neural networks the important role play single-vortex intradot magnetostatic modes. Several aspects of the model have been understood by the numerical study using the simulated annealing method.