Variational Autoencoder-Based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines

Abstract

Conventional magneto-static finite element (FE) analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This article presents a novel method for predicting key performance indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high-dimensional integrated design parameters in a lower-dimensional latent space using a variational autoencoder (VAE). After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization.