Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
Abstract
Conventional magneto-static finite element 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 paper 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. 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.
Cite
@article{arxiv.2201.08877,
title = {Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines},
author = {Vivek Parekh and Dominik Flore and Sebastian Schöps},
journal= {arXiv preprint arXiv:2201.08877},
year = {2022}
}
Comments
4 pages, 15 This article will appear in the proceedings of the IEEE transactions on magnetics as a conference paper