Optimization of rotating electrical machines is both time- and computationally expensive. Because of the different parametrization, design optimization is commonly executed separately for each machine technology. In this paper, we present the application of a variational auto-encoder (VAE) to optimize two different machine technologies simultaneously, namely an asynchronous machine and a permanent magnet synchronous machine. After training, we employ a deep neural network and a decoder as meta-models to predict global key performance indicators (KPIs) and generate associated new designs, respectively, through unified latent space in the optimization loop. Numerical results demonstrate concurrent parametric multi-objective technology optimization in the high-dimensional design space. The VAE-based approach is quantitatively compared to a classical deep learning-based direct approach for KPIs prediction.

翻译：旋转电机的优化既耗时又计算成本高昂。由于参数化方式不同，各电机技术的设计优化通常独立进行。本文提出采用变分自编码器（VAE）同时优化异步电机和永磁同步电机两种不同技术。训练后，我们分别利用深度神经网络和Decoder作为元模型，通过优化循环中的统一潜空间预测全局关键性能指标（KPI）并生成相关新设计。数值结果表明，该方法可在高维设计空间中实现并发参数化多目标技术优化。本文还将基于VAE的方法与经典深度学习直接方法进行了KPI预测的定量比较。