  The impact of a drop on a solid surface is an important phenomenon that has
various implications and applications. However, the multiphase nature of this
phenomenon causes complications in the prediction of its morphological
evolution, especially when the drop splashes. While most machine-learning-based
drop-impact studies have centred around physical parameters, this study used a
computer-vision strategy by training an encoder-decoder to predict the drop
morphologies using image data. Herein, we show that this trained
encoder-decoder is able to successfully generate videos that show the
morphologies of splashing and non-splashing drops. Remarkably, in each frame of
these generated videos, the spreading diameter of the drop was found to be in
good agreement with that of the actual videos. Moreover, there was also a high
accuracy in splashing/non-splashing prediction. These findings demonstrate the
ability of the trained encoder-decoder to generate videos that can accurately
represent the drop morphologies. This approach provides a faster and cheaper
alternative to experimental and numerical studies.
