论文标题
机器学习并预测干泡沫中局部屈服的时间依赖性动态
Machine learning and predicting the time dependent dynamics of local yielding in dry foams
论文作者
论文摘要
干泡沫的产量是通过气泡刻度“ T1” s的小基本产量事件来实现的。我们使用人工智能（AI）和最近的邻居分析研究了在扩展的2D流程几何形状中对这些检测的大规模检测。 AI方法仅使用单个帧就达到了良好的准确性，顶点中心图像的最高分数突出了顶点在泡沫局部产量中所扮演的重要作用。我们提前研究了T1的可预测性，并表明这在与当地社区T1的等待时间统计有关的时间表上是可能的。局部T1事件可预测性的发展是不对称的，并测量了局部特性的变化，并同样地存在局部屈服后的放松时间尺度的存在。
The yielding of dry foams is enabled by small elementary yield events on the bubble scale, "T1"s. We study the large scale detection of these in an expanding 2D flow geometry using artificial intelligence (AI) and nearest neighbour analysis. A good level of accuracy is reached by the AI approach using only a single frame, with the maximum score for vertex centered images highlighting the important role the vertices play in the local yielding of foams. We study the predictability of T1s ahead of time and show that this is possible on a timescale related to the waiting time statistics of T1s in local neighborhoods. The local T1 event predictability development is asymmetric in time, and measures the variation of the local property to yielding and similarly the existence of a relaxation timescale post local yielding.
