Constraint free physics-informed machine learning for micromagnetic energy minimization

Abstract

We introduce a novel method for micromagnetic energy minimization which uses physics-informed neural networks to find a magnetic configuration which minimizes the Gibbs Free energy functional without the need of any constraint optimization framework. The Cayley transform is applied to a neural network to assure that the model output lives on the Lie group of rotation matrices SO(3). For the stray field computation we use the splitting ansatz of Garcia-Cervera and Roma together with a hard constraint extreme learning machine in combination with a Taylor series approximation for very accurate evaluation of the single layer potential which only requires a very coarse discretization of the surface. Further, we present a modeling framework for constructive solid geometry which uses R-functions to exactly satisfy essential boundary conditions arising in the course of stray field computation. This framework can be applied to many other areas of interest. Our method shows promising results on the NIST μMAG Standard Problem #3, and is also applied to compute the demagnetization process of a hard magnetic Nd2Fe14B cube.