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.

翻译：液滴撞击固体表面是一种具有多种影响和广泛应用的重要现象。然而，该现象的多相流本质导致其形态演化预测存在复杂性，尤其在液滴发生飞溅时。尽管多数基于机器学习的液滴撞击研究集中于物理参数分析，本研究采用计算机视觉策略，通过训练编码器-解码器利用图像数据预测液滴形态。我们证明，该训练后的编码器-解码器能够成功生成展示飞溅与非飞溅液滴形态的视频。值得注意的是，在这些生成视频的每一帧中，液滴的铺展直径与实际视频数据高度吻合。此外，飞溅/非飞溅预测的准确率同样极高。这些发现表明，经过训练的编码器-解码器具有生成准确呈现液滴形态视频的能力。该方法为实验研究和数值模拟提供了更快速、更低成本的替代方案。