Showing papers by "Martin Gmitra published in 2002"

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

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.

Neural network analysis of the magnetization reversal in magnetic dot arrays

Abstract: 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 40 000 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.

The Evolutionary Approach to the Optimization of Finite-Size Effects in Magnetic Dot Arrays

Abstract: We considered simple 2D model of the magnetic dot array with uniaxial anisotropic dots. For this model we formulated the inverse magnetic problem, where desired static ground-state properties are attained by varying directions of the local anisotropy vectors. The problem is investigated numerically using a method based on evolutionary optimization.