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虽然变分自编码器(VAEs)代表了一个广泛的有影响力的深度生成模型,但潜在的能量函数的许多方面仍然知之甚少。特别是,一般认为高斯编码器/解码器的假设降低了VAEs生成真实样本的有效性。在这方面,我们严格地分析VAE目标,区分哪些情况下这个信念是真实的,哪些情况下不是真实的。然后我们利用相应的见解来开发一个简单的VAE增强,不需要额外的hyperparameters或敏感的调优。在数量上,这个提议产生了清晰的样本和稳定的FID分数,这些分数实际上与各种GAN模型相竞争,同时保留了原始VAE架构的理想属性。这项工作的一个简短版本将出现在ICLR 2019年会议记录(Dai和Wipf, 2019)上。我们模型的代码在这个https URL TwoStageVAE中可用。

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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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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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