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 ...

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 ...

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However ... The dimension of the latent space is set to 2. The variational autoencoder with ...

A variational autoencoder (VAE) is a deep neural system that can be ... file of UCI digits data into memory as a two-dimensional array using the NumPy loadtxt() function. The pixel values are ...

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared ... efforts to complement an autoencoder with an advanced type of autoencoder ...