自编码器 - 专知

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在本文中，我们将研究自动编码器。这篇文章涵盖了数学和自动编码器的基本概念。我们将讨论它们是什么，限制是什么，典型的用例，我们将看一些例子。我们将从对自动编码器的一般介绍开始，并讨论激活函数在输出层和损耗函数中的作用。然后我们将讨论什么是重构误差。最后，我们将看看典型的应用，如降维、分类、去噪和异常检测。

https://www.zhuanzhi.ai/paper/b868e59690a0abbee2c9715f983a36f8

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1 月 21 日

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

Vivek Parekh,Dominik Flore,Sebastian Schöps

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

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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1 月 21 日

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

Vivek Parekh,Dominik Flore,Sebastian Schöps

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

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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