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

• URL: http://arxiv.org/abs/2201.08877v1
• Date: Fri, 21 Jan 2022 19:49:54 GMT
• Title: Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
• Authors: Vivek Parekh, Dominik Flore, Sebastian Sch\"ops
• Abstract summary: This paper presents a novel method for predicting Key Performance Indicators (KPIs) of differently parameterized electrical machine topologies at the same time. 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 topology, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conventional magneto-static finite element analysis of electrical machine models 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.

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