A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

Besides, the loss function of the variational autoencoder is revised and improved. The aim is to learn feature representations with fewer image features to obtain more accurate results. (2) In the ...

Variational Autoencoder-Based Metamodeling for Multi-Objective Topology Optimization of Electrical ... After training, via a latent space, the decoder and multi-layer neural network will function as ...

Conventional magneto-static finite element (FE) analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, ...

The variational autoencoder with 4 hidden layers performed the best with high Spearman and Pearson coefficients and low RMSD. In terms of the encoder ( Figures 4A,B ), a larger number of layers lead ...

Combining the mean and log-variance in this way is called the reparameterization trick. The discovery of this idea in the original 2013 research paper ("Auto-Encoding Variational Bayes" by D.P. Kingma ...

Listing 5: Function make_err_list() to Compute Reconstruction Errors. ... There are research efforts to complement an autoencoder with an advanced type of autoencoder called a variational autoencoder ...