论文标题
基于变异自动编码器的元模型，用于电机的多目标拓扑优化
Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
论文作者
论文摘要
电机设计的常规磁静态有限元分析是耗时的，计算上昂贵。由于每个机器拓扑都有一组不同的参数，因此设计优化通常是独立执行的。本文提出了一种新的方法，用于同时使用差异自动编码器在较低维度的潜在空间中绘制高维积分设计参数的同时预测不同参数化电机拓扑的关键性能指标（KPI）。在训练之后，通过潜在空间，解码器和多层神经网络将作为元模型，分别用作对新设计和预测相关KPI的元模型。该启用基于参数的并发多元素优化。
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.
