  We introduce a two-step, fully reversible process designed to project the
outer shape of a generic droplet onto a lower-dimensional space. The initial
step involves representing the droplet's shape as a Fourier series.
Subsequently, the Fourier coefficients are reduced to lower-dimensional vectors
by using autoencoder models. The exploitation of the domain knowledge of the
droplet shapes allows us to map generic droplet shapes to just 2D space in
contrast to previous direct methods involving autoencoders that could map it on
minimum 8D space. This 6D reduction in the dimensionality of the droplet's
description opens new possibilities for applications, such as automated droplet
generation via reinforcement learning, the analysis of droplet shape evolution
dynamics and the prediction of droplet breakup. Our findings underscore the
benefits of incorporating domain knowledge into autoencoder models,
highlighting the potential for increased accuracy in various other scientific
disciplines.
