An inverse design method for electric machines based on conditional variational autoencoder

Abstract

Electric machines play a key role in electromechanical energy conversion across modern industrial systems. Conventional design methodologies typically involve many repetitive tasks that exhibit low computational efficiency. To address this challenge, this paper proposes an inverse design system based on conditional variational autoencoder (CVAE), which establishes a direct mapping relationship between predefined performance indicators and optimal structural parameters. The main contributions are threefold: First, a proportional modeling approach is conducted that ensures the inverse design outcomes are free from geometric interferences. Second, a surrogate model is integrated to enhance data quality, combined with a tailored loss function that simultaneously optimizes predictive accuracy and solution diversity. Third, an algorithm is implemented to satisfy nonlinear inequality constraints in practical design scenarios. The test results demonstrate that the proposed system can efficiently generate diverse machine designs that meet specified key performance indicators (KPIs), validating its effectiveness in accelerating electric machine design.