Year: 2021
Type: article
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.
Source: IEEE Transactions on Magnetics
Institution: Technical University of Denmark
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3164220104