Fig. 5: Schematic diagram of the WGAN-GP model employed herein.

The generator takes a multivariate Gaussian distributed random noise z (latent variable) as input and is trained to learn the mapping to the data distribution of existing alloy samples. The path of error backpropagation is denoted by the red dashed arrows.