Machine learning techniques have been recently applied in predicting deformation in amorphous materials. In this study, we extract structural features around liquid film vertices from images of flowing 2D foam and apply a multilayer perceptron to predict local yielding. We evaluate their importance in the description of the T1 events and show that a high level of predictability may be achieved using well-chosen combinations of features as the prediction data. The most relevant features are extracted by performing the predictions separately for isolated sets of features, and these findings are verified using principal component analysis. Using this approach, we determine which properties of the images are most important with regard to the physics of the processes. Our findings indicate that film lengths and angles between the liquid films joining at the vertex are the most important features that predict the local yield events. These two features describe 83% of the yield events. As an application, we extract the statistics of event waiting times from the experiment.

中文翻译：

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从几何特征预测和跟踪干泡沫中的 T1 事件

机器学习技术最近已应用于预测非晶材料的变形。在这项研究中，我们从流动的二维泡沫图像中提取液膜顶点周围的结构特征，并应用多层感知器来预测局部屈服。我们评估了它们在 T1 事件描述中的重要性，并表明使用精心挑选的特征组合作为预测数据可以实现高水平的可预测性。通过对孤立的特征集分别执行预测来提取最相关的特征，并使用主成分分析验证这些发现。使用这种方法，我们可以确定图像的哪些属性对于过程的物理学最重要。我们的研究结果表明，在顶点处连接的液膜之间的膜长度和角度是预测局部屈服事件的最重要特征。这两个特征描述了 83% 的屈服事件。作为应用程序，我们从实验中提取事件等待时间的统计信息。