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.

本文介绍了应用变分自动编码器来同时优化异步机和永磁同步机，利用深度神经网络和解码器作为元模型来预测全局关键绩效指标并在优化循环中通过统一的潜空间生成相关的新设计。数值结果证明了高维设计空间中并行的多目标技术优化。同时，与基于经典深度学习的直接方法相比，基于 VAE 的方法在 KPI 预测方面具有量化优势。