IDM-Net: A Multi-Task Supported Encoder-Decoder Framework for Magnetic Field Inverse Design

Abstract

We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different types of magnet shapes. The decoder focuses on inferring significant properties of each specific type of magnet shape, including size, position, and magnetization intensity. Such architecture breaks the critical limitation of designing only a single type of magnet in literature. Further, it allows for flexible choices of encoders' networks, such as convolutional neural networks (CNNs) or transformers, which are widely used in various computer vision tasks. Our experimental results demonstrate that the ResNet50-based and ViT-B/16-based IDM-Nets achieve accuracies of 93.8% and 91.4% in magnet shapes classification and errors of 0.31% and 0.33% in predicting magnetic properties, respectively.