  Magneto-static finite element (FE) simulations make numerical optimization of
electrical machines very time-consuming and computationally intensive during
the design stage. In this paper, we present the application of a hybrid
data-and physics-driven model for numerical optimization of permanent magnet
synchronous machines (PMSM). Following the data-driven supervised training,
deep neural network (DNN) will act as a meta-model to characterize the
electromagnetic behavior of PMSM by predicting intermediate FE measures. These
intermediate measures are then post-processed with various physical models to
compute the required key performance indicators (KPIs), e.g., torque, shaft
power, and material costs. We perform multi-objective optimization with both
classical FE and a hybrid approach using a nature-inspired evolutionary
algorithm. We show quantitatively that the hybrid approach maintains the
quality of Pareto results better or close to conventional FE simulation-based
optimization while being computationally very cheap.
