  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.
