A comparison of neural network and polynomial models for the approximation of non-linear and anisotropic ferromagnetic materials

Polynomials fail to give suitable approximations for strongly nonlinear functional mappings. In that case, neural networks can preferably be used. Over the past decade, their popularity steadily increased within various engineering disciplines. Here, it is shown that neural networks must not always be preferred over traditional polynomials. When modeling typical nonlinear and anisotropic magnetic properties for e.g. finite element simulations, both approximations are fairly competitive.