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.

4 pages, 15 This article will appear in the proceedings of the IEEE transactions on magnetics as a conference paper

References in corpus (1)

• Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine

Cited by in corpus (3)

• Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model
• Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines
• DADO -- Low-Cost Query Strategies for Deep Active Design Optimization