Inverse Design of Magnetic Fields Using Deep Learning
Stefan Pollok, Rasmus Bjørk, Peter Stanley Jørgensen
Abstract
We here present a novel deep learning (DL) approach for designing structures of permanent magnets. The challenge for the DL method in this kind of problem is to learn the mapping from a desired magnetic field to a simple magnetic structure, i.e., an inverse design approach. We demonstrate this approach by training six different standard convolutional neural network (CNN) structures previously used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) to inversely predict the properties of a single hard magnet (magnetization, size, and location) from a given 2-D magnetic field. We show that the best network, ResNeXt-50, can perform this prediction with an error of 0.22% in the properties of the magnet.